A graduate student has developed a deep learning algorithm that can detect seizures from EEG data with 95% accuracy. It works beautifully in Python on a laptop. Now comes the hard part: making it run on a chip small enough to wear on your head, using milliwatts instead of watts, and producing results in real-time instead of after-the-fact analysis. That's your challenge.
You'll take trained neural network models and implement them on embedded hardware platforms—either FPGAs for custom hardware acceleration or microprocessors for flexible deployment. For FPGA implementation, you'll translate network architectures into Verilog or VHDL, designing custom data paths for convolutional layers, activation functions, and pooling operations. You'll optimize for the unique constraints of medical wearables: minimize power consumption, meet real-time latency requirements (results needed within seconds, not minutes), and fit within available logic resources.
For microprocessor implementation, you'll convert high-level models into optimized embedded C/C++, using fixed-point arithmetic instead of floating-point, implementing custom quantization schemes that reduce memory footprint while preserving accuracy, and leveraging hardware accelerators (DSP units, SIMD instructions) available on the target processor. You might explore neuromorphic architectures—spiking neural networks that naturally map to low-power event-driven hardware.
You'll validate implementations against the original algorithms, measuring not just accuracy but also power consumption, latency, and resource utilization. Can you maintain 95% accuracy while using 1000× less power? Can you process a full second of EEG data in under 100 milliseconds? These are the tradeoffs you'll explore.
This isn't theoretical—you'll be working with our actual custom chips and development boards, implementing algorithms destined for real medical devices worn by epilepsy patients.
Research area, student roles & skills
Research area: Our laboratory develops machine learning algorithms that detect seizures and remove motion artifacts from brain signals in real-time. These algorithms—developed by our graduate students in Python and TensorFlow—must ultimately run on low-power embedded hardware inside a wearable medical device. This creates a fascinating hardware implementation challenge: translating trained neural networks into optimized HDL (Verilog/VHDL) or embedded C code that executes on FPGAs or microprocessors with strict power, latency, and accuracy constraints. This summer, we're implementing our latest AI algorithms on target hardware platforms, bridging the gap between machine learning research and deployable medical devices.
Student roles: You'll serve as the critical link between our machine learning researchers and our hardware platform, translating trained AI models into optimized implementations that run on embedded devices with strict power and latency constraints.
Algorithm Analysis and Profiling (20%): Start by deeply understanding the target algorithm—a seizure detection or artifact removal neural network developed by our graduate students. You'll profile the Python/TensorFlow implementation to identify computational bottlenecks: which layers consume the most operations? Where is memory bandwidth limiting? What numerical precision is actually required? This analysis guides your optimization strategy.
Hardware Implementation (50%): Choose your path based on the target platform and your expertise: FPGA Route: Design custom hardware accelerators in Verilog/VHDL. Implement parameterized modules for convolutional layers, activation functions, and fully connected layers. Create optimized data paths that process multiple operations in parallel, implement custom fixed-point arithmetic units, and design memory controllers that efficiently stream EEG data through the network. Use high-level synthesis tools where appropriate to accelerate development.
Microprocessor Route: Optimize algorithm implementation for embedded ARM Cortex-M processors or similar targets. Implement custom quantization schemes (8-bit or 16-bit fixed-point), leverage CMSIS-NN libraries and hardware accelerators, use look-up tables for nonlinear functions, and implement efficient memory management for layer-by-layer execution. Validation and Optimization (30%): Verify that your implementation matches the original algorithm's accuracy using our test datasets. Measure power consumption, execution latency, and resource utilization. Iterate on the design: can you reduce power by lowering precision? Can you meet latency requirements by adding parallelism? Document tradeoffs between accuracy, speed, power, and area.
You'll present weekly progress updates and prepare comprehensive documentation of your implementation. This role offers deep experience in hardware/software co-design, AI acceleration, and the unique challenges of deploying machine learning in resource-constrained medical devices.
Skills required: Strong programming skills in C/C++ and Python Experience with either: (1) HDL design (Verilog/VHDL) and FPGA development, OR (2) embedded systems optimization and microcontroller programming Basic understanding of machine learning concepts and neural network architectures Familiarity with fixed-point arithmetic and numerical optimization Experience with development tools (Vivado, Quartus, or embedded IDEs) Signal processing fundamentals helpful Strong debugging and systematic testing abilities Interest in hardware acceleration and low-power design Ability to bridge software algorithms and hardware constraints
2. Adding Motion Sensing to Brain Monitoring: Multi-Sensor Hardware Integration
When patients move their heads, electrodes shift against the scalp, creating electrical artifacts that can overwhelm genuine brain signals. Our AI algorithms can remove these artifacts—but only if they know when movement is occurring. Your mission is to add accelerometer sensing to our system, providing that crucial motion context.
This is primarily an embedded systems project with some PCB integration work. You'll select appropriate 3-axis MEMS accelerometer modules based on sensitivity requirements, sampling rate capabilities, and power consumption constraints. Then you'll integrate them into our existing backend hardware module—the central unit that collects data from all 21 electrodes.
The embedded programming challenge is synchronization. Our system already captures EEG signals and electrode impedance measurements at precise timestamps. You'll implement firmware that reads accelerometer data via I2C or SPI communication and timestamps it with the same clock reference used for EEG data. Even millisecond timing errors could make the data useless for artifact detection, so you'll implement interrupt-driven sampling and hardware timer synchronization.
You'll develop data buffering and transmission routines that package accelerometer readings alongside EEG data, creating unified data packets that our AI algorithms can process. This involves designing efficient data structures, implementing circular buffers for real-time streaming, and ensuring the additional data doesn't overload our wireless communication bandwidth.
Testing is critical: you'll use motion platforms and signal generators to verify that accelerometer data correctly correlates with induced motion artifacts in the EEG. You'll validate timing accuracy using oscilloscopes and logic analyzers, and conduct long-term recording tests to ensure stable operation over 24-hour monitoring sessions.
Your work creates the hardware foundation that enables our AI team to develop smarter artifact removal algorithms—you're building the sensing infrastructure they need.
Research area, student roles & skills
Research area: Our lab is developing wearable brain monitoring systems that can distinguish genuine brain activity from motion artifacts during daily activities. To improve artifact detection, we're expanding our sensor array beyond EEG electrodes to include accelerometer data. This creates a multi-modal sensing system where motion information helps our AI algorithms identify when signal interference is caused by patient movement versus actual neurological events. This summer, we're integrating 3-axis accelerometers into our backend module, creating synchronized data streams that combine brain signals, electrode impedance measurements, and motion data—all time-aligned for processing by our machine learning artifact removal algorithms.
Student roles: You'll lead the accelerometer integration into our wearable EEG backend module, focusing on hardware interfacing and embedded firmware development to create synchronized multi-sensor data streams.
Hardware Integration (30%): Select appropriate MEMS accelerometer modules based on technical requirements: ±2-4g range for head movement detection, sampling rates matching our EEG system (250-1000 Hz), low power consumption for battery operation, and I2C/SPI digital interfaces. You'll integrate the chosen sensor into our existing PCB or create a small daughter board, handle power supply requirements, and ensure proper electrical connections. You'll also implement any necessary signal conditioning or voltage level translation.
Embedded Firmware Development (50%): Implement the core accelerometer acquisition firmware. You'll write I2C or SPI communication drivers to read sensor data, develop interrupt-driven sampling routines that trigger precisely synchronized with EEG data collection, and implement hardware timer-based synchronization to ensure all sensor modalities share a common timestamp reference. You'll create efficient data buffering systems using circular buffers or DMA transfers, design data packet structures that combine accelerometer and EEG data, and implement the transmission protocol that sends unified multi-sensor data to our processing algorithms.
Testing and Validation (20%): Verify synchronization accuracy using oscilloscopes and logic analyzers—measuring the actual timing relationship between EEG samples and accelerometer readings. Conduct motion correlation tests where controlled head movements should produce corresponding accelerometer signals and EEG artifacts. Perform long-term stability testing to ensure 24+ hour continuous operation without timing drift or data loss. Document performance characteristics including sampling accuracy, timing precision, and power consumption impact.
You'll work closely with our AI team to ensure data format compatibility and with the systems integration team for backend module integration. This role provides hands-on experience with multi-sensor embedded systems, real-time programming, and biomedical device development.
Skills required: Strong embedded C/C++ programming skills for microcontrollers Experience with I2C and SPI communication protocols Understanding of real-time systems and interrupt-driven programming PCB integration experience (component selection, soldering, basic layout) helpful Familiarity with timing-critical sensor synchronization Debugging skills using oscilloscopes and logic analyzers Basic understanding of signal processing concepts (sampling rates, aliasing) Systematic testing and documentation abilities Interest in biomedical sensor systems Ability to work independently while collaborating with AI and hardware teams
3. Advanced Biosensing Technologies for Early Diagnosis and Therapeutic Monitoring of Neurodegenerative Diseases
Chronic diseases, including neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, represent a growing global health challenge and place a substantial burden on healthcare systems worldwide. Early diagnosis and timely intervention can significantly improve patient outcomes while reducing healthcare costs. However, current diagnostic and monitoring approaches often rely on complex, time-consuming, and expensive laboratory techniques that are not easily accessible in decentralized healthcare settings.
To address these limitations, there is a critical need for innovative, cost-effective, and highly sensitive point-of-care (POC) technologies capable of detecting disease-specific biomarkers without the need for labeling. Label-free biosensors offer a promising alternative by enabling rapid, accurate, and real-time detection of biomolecular interactions while reducing assay complexity and cost. Their widespread adoption could facilitate earlier diagnosis, improve disease monitoring, and support more efficient healthcare delivery.
This project aims to develop a novel label-free point-of-care biosensing platform for the early diagnosis and monitoring of neurodegenerative diseases, with an initial focus on Alzheimer’s and Parkinson’s disease. The short-term objectives include identifying and validating clinically relevant biomarkers and biorecognition strategies compatible with our sensing technology, followed by the design, fabrication, and characterization of an innovative biosensor capable of detecting these biomarkers with high sensitivity and specificity. Particular attention will be devoted to optimizing device performance through the refinement of sensor architecture, materials, and operational parameters.
In the longer term, this research seeks to establish a fully integrated, portable, and user-friendly diagnostic platform suitable for clinical and decentralized healthcare environments. By enabling rapid, reliable, and cost-effective biomarker detection, the proposed technology has the potential to improve early diagnosis, support personalized disease management, and contribute to better health outcomes for patients affected by neurodegenerative disorders.
Research area, student roles & skills
Research area: My research program focuses on the development of next-generation biosensing technologies for the early detection, monitoring, and personalized treatment of chronic diseases, including cancer and neurodegenerative disorders. In Dr. Jafari’s laboratory, we design, fabricate, and validate advanced biosensor platforms capable of detecting and quantifying disease-related biomarkers in biological fluids with high sensitivity and specificity. These technologies enable rapid and accurate diagnosis, prognosis, and treatment monitoring. We also develop biosensors for therapeutic drug monitoring in blood and serum, supporting personalized medicine by optimizing drug dosage, maintaining therapeutic efficacy, and minimizing adverse effects.
Student roles: The student will assist with both simulation and experimental work related to the development of biosensors for the early diagnosis and personalized treatment of chronic diseases such as cancer and Alzheimer’s. Their role will include the analysis, design, and simulation of various biosensors based on different technologies. They will also support the development, fabrication, and characterization of the designed biosensors. The student will receive full training and work under the supervision of experienced team members. They will contribute to data collection, participate in lab meetings to discuss findings, and help interpret the obtained results. As they gain experience, they may also assist with protocol development or troubleshooting. This role is designed to provide the student with hands-on experience in biosensor design and development, while contributing meaningfully to the lab’s overall research objectives.
Skills required: Previous engineering or laboratory experience is not required, as all necessary technical skills will be taught during the internship. The student will learn how to simulate biosensor structures and analyze their performance, as well as gain familiarity with fabrication processes and the functionality of these biosensors. While an interest in biosensors and engineering concepts is beneficial, the most important qualities are curiosity, responsibility, and a strong willingness to learn. The student should also be comfortable working in a team environment and capable of carefully following established protocols.
This project will explore the capacities of analogue electronic hardware versions of neural nets. It will involve the design and construction of a multi-neuron neural network in hardware circuitry. In particular, a three-layer neural net will be constructed. A n approach to the construction of backpropagation circuitry will be built and demonstrated. Such a hardware circuit can potentially be 3D printed. Furthermore, these architectures are immune to cyber-attack.
Research area, student roles & skills
Research area: There is interest in developing innovative approaches to computing including artificial intelligence. Neural networks are one such approach - they are Turing complete in offering the same capacities as traditional CPU-based computer architectures. Indeed, deep learning that has been advancing artificial intelligence has demonstrated the power of neural networks. It is of interest to implement rule-based AI systems in neural form and vice versa - so-called hybrid systems. There is the further prospect that analogue electronics implementations of neural nets offer enhanced capabilities over software implementations.
Student roles: The student will design and construct these circuits and test them.
Skills required: Electronics design Electronic circuit construction
5. CPS: Cyber physical System
Supervisor: Ahmed Lakhssassi
University: Université du Québec en Outaouais (Gatineau campus)
Research area: In a world where Cyber-Physical Systems (CPS) have become ubiquitous, their security has become a major concern. CPS, which integrate physical components and interconnected computer systems, are essential in various fields such as energy, healthcare, manufacturing, and transportation. However, the increasing complexity of these systems makes them vulnerable to a multitude of threats, including thermal attacks, which exploit temperature variations to compromise their integrity. In this context, our innovative project emerges to strengthen the security of CPS by introducing Thermal Digital Twins (TDT).
Student roles: Contribute ton the project.
Skills required: Cyber-Physical Systems (CPS)
6. Characterization of a hybrid MEMS graphene resonator
Supervisor: Frederic Nabki
University: École de Technologie Supérieure (Montréal campus)
Microelectromechanical systems (MEMS) resonators have emerged as powerful tools for sensing and signal processing at the microscale, offering high sensitivity, small footprints, and low power consumption. Recent advancements in material science have opened new possibilities for enhancing MEMS performance through the integration of two-dimensional (2D) materials. Among these, graphene stands out due to its exceptional mechanical, electrical, and thermal properties.
This research project focuses on the development and characterization of a novel hybrid MEMS resonator that combines a monolayer of graphene with conventional silicon microstructures. The aim is to exploit the synergistic advantages of graphene’s low mass density and high Young’s modulus with the well-established fabrication and integration benefits of silicon. This hybrid platform is designed to enhance the performance of MEMS resonators in terms of frequency stability, sensitivity, and functional tunability.
A central objective of this work is to systematically characterize the mechanical and electromechanical behavior of the hybrid resonator across a range of operating conditions. Key parameters such as resonance frequency, quality factor will be measured and analyzed.
Ultimately, this research aims to push the boundaries of MEMS performance.
Research area, student roles & skills
Research area: My research interests include microelectromechanical systems (MEMS) and RF/analog microelectronics, specifically focusing on the creation of next-generation MEMS processes using advanced materials, the integration of MEMS devices with CMOS systems, the modeling of MEMS devices, and the design of CMOS phase-locked loops, ultra-wideband transceivers, and MEMS interface circuits.
My overarching goal is to create innovative integrated systems that achieve unprecedended levels of miniaturization and performance for applications such as sensors or communication systems.
Student roles: Do a brief litterature survey. Characterization of the MEMS Design electronic circuits. Simulate their behavior. If time permits, send a device out to micro-fabrication or a circuit to fabrication. Some laboratory hands-on testing of existing devices may also be done.
Skills required: Some knowledge of eletronics with ideally basic knowledge of classical mechanics, ultrasounds or waves. Knowledge of micro-mechanical modeling using computer aided design tools such as ANSY, Comsol or Coventorware are assets but are not necessary as training can be done during the intership.
7. Custom-Fit Wearables: Engineering Comfort for 24-Hour Brain Monitoring
Try wearing a bike helmet for 24 hours. Now imagine that helmet needs to position 21 electrodes on your scalp with millimeter precision, maintain electrical contact through your hair, remain comfortable while you sleep, and be easy enough to remove and reapply that you can do it yourself each morning. That's the engineering challenge we're solving.
Your project focuses on the mechanical design and fabrication of our wearable EEG headset, with emphasis on electrode integration and patient comfort. You'll work with 3D scanning data of actual human heads, using CAD software to design custom-fit caps that maintain the international 10-20 electrode positioning standard while accommodating individual anatomy. This involves parametric modeling—creating designs that can be automatically customized for different head shapes and sizes.
The electrode integration challenge is particularly interesting. Each electrode contains custom chips and must maintain stable electrical contact with the scalp through hair. You'll design mounting systems that allow controlled electrode compression against the scalp without causing discomfort, explore mechanisms that accommodate different hair types and densities, and develop solutions that maintain position accuracy during head movement. You'll iterate through multiple 3D printing materials and techniques (stereolithography, selective laser sintering, flexible materials) to find optimal combinations of rigidity and compliance.
Testing is hands-on: you'll create prototypes, recruit volunteers to wear them, collect feedback on comfort and usability, and measure whether electrodes maintain position during normal activities. You'll use 3D scanning to verify that electrodes land correctly after repeated application, conduct long-term wear tests to assess comfort over hours, and evaluate how easily non-expert users can apply and remove the headset. Your work directly influences whether patients will actually use this device in their daily lives.
Research area, student roles & skills
Research area: The greatest technical challenge in wearable medical devices isn't often the sensors or algorithms—it's getting patients to actually wear them. Our lab combines biomechanical engineering with user-centered design to create EEG headsets that patients can comfortably wear for days at a time. Using 3D scanning, parametric CAD modeling, and advanced additive manufacturing, we create custom-fitted headsets that maintain precise electrode positioning while adapting to individual head shapes. This summer, we're refining our headset design and electrode integration strategy, working toward a medical device that balances clinical precision with real-world wearability.
Student roles: You'll lead the mechanical design refinement of our wearable EEG headset, focusing on the critical interface between our intelligent electrodes and the patient's head. This role combines engineering design, human factors research, and hands-on prototyping. CAD Design and Modeling (40%): Develop parametric CAD models for custom-fitted EEG caps using SolidWorks or similar professional tools. You'll work from 3D scans of human heads, creating designs that automatically adapt to different anatomies while maintaining precise electrode positions. You'll design modular components—electrode mounts, cap structures, connection systems—that can be customized and assembled in different configurations. This includes stress analysis to ensure structural integrity while minimizing weight, and ergonomic analysis to identify pressure points and improve comfort. Electrode Integration (35%): Design mechanical systems that hold our Active Intelligent Electrodes in correct positions while maintaining reliable scalp contact. You'll create spring-loaded mounting systems, flexible prongs that penetrate hair, and hydrogel electrode interfaces—all while accommodating the electronics inside each AIE. You'll collaborate with the electrical engineering team to understand electrode constraints (size, weight, connection points) and ensure your mechanical designs don't interfere with electrical functionality. Prototyping and Testing (25%): Transform CAD models into physical prototypes using the lab's 3D printers and other fabrication equipment. You'll iterate through multiple designs based on fit testing with volunteers, comfort assessments during extended wear periods, and electrode positioning accuracy measurements. You'll conduct structured user studies, collecting both quantitative data (position accuracy, application time) and qualitative feedback (comfort ratings, usability concerns). You'll document your work in professional design reports, present concepts to the team for feedback, and prepare manufacturing specifications for final prototypes. This role offers experience in medical device design, user-centered engineering, and the intersection of mechanical systems with bioelectronics—skills highly valued in the growing wearable medical device industry.
Skills required: Proficiency in 3D CAD software (SolidWorks, Fusion 360, or similar parametric modeling tools) Experience with 3D printing and understanding of different additive manufacturing processes Mechanical design fundamentals (material selection, stress analysis, ergonomics) Interest in biomedical or wearable device design Creative problem-solving for user-centered design challenges Strong attention to detail and iterative design thinking Ability to conduct user testing and incorporate feedback Background in mechanical, biomedical, or industrial design engineering Excellent communication skills for presenting design concepts
8. Design and development of a haptic device for force feedback on fingers.
Summary: In this project, a novel haptic device will be designed and developed for applying haptic force feedback on fingers. Different types of mechanisms such as tendon-driven or direct actuation mechanism might be used in the design. A device with multiple actuators and sensing units distributed on its back will designed and fabricated. The system’s response tests and calibration experiments will be conducted to evaluate the feasibility and functionality of the system.
Research area, student roles & skills
Research area: • Surgical Robotic Systems • Smart Surgical Tool Design
• Haptic-Enabled Telerobotics • Robot-Assisted Space Telemetry
• Mechatronics in Surgical Skill Assessment • Hydraulic Robotic Manipulators
• Virtual Fixtures in Telerobotic Systems • Mechatronics in Medical Training
• Nonlinear Dynamic Systems • Lyapunov Stability Analysis
• Haptic and Tactile Feedback • Haptic Hand-Controllers
• MR-Compatible Robotic Systems • Micromanipulation
• Nonlinear Dynamic Systems • Filippov Solutions
• Stability of Dynamic Systems with Discontinuity Surfaces • Delay in Telerobotic Systems
• Image-Guided Robotic Systems • Robotic Systems for Rehabilitation
• Dynamic Modelling of Telerobotic Systems • Force-Based Virtual Fixtures
Student roles: The student will design and develop a functional prototype of the system.
Unlike conventional cameras, which record all light incident onto a pixel, our coded-exposure-pixel (CEP) cameras can be programmed to selectively sort the light based on its path or time of travel. In conjunction with a concurrently coded illumination, this enables a wide range of previously unattainable video capabilities such as seeing against the sun, seeing through the skin, or seeing behind an object/around the corner.
This interdisciplinary project spans across many fields such as the design of analog and digital integrated circuits for custom-fabricated CMOS image sensors, embedded systems with high-performance FPGAs, semiconductor devices such as novel 3D photodetectors, and development and deployment of computational-imaging and computer vision algorithms and models, as well as various aspects of optics and photonics. The interdisciplinary nature of our team enables all members to focus on their field of interest, and to have exposure to other disciplines.
Students will actively participate in the development of computational imaging systems, taking ownership of specific modules within the broader hardware and software architecture. To support the project's interdisciplinary scope, individuals will be assigned to tackle one or more specialized tasks, such as digital and analog ASIC design, FPGA firmware development, semiconductor device characterization, optics integration, or computer vision implementation, based on immediate project demands.
Research area, student roles & skills
Research area: In a long-standing scientific collaboration between researchers in the Departments of Electrical and Computer Engineering and Computer Science at the University of Toronto and jointly with several world’s leading research centers in Computer Vision and Robotics, our team is leading the development of a new family of coded-pixel cameras with never-before-seen capabilities, like the ability to sort incoming photons based on their properties. These cameras target the growing space of new computer vision applications such as 3D imaging for robotic navigation of drones and self-driving cars in harsh environments; and next-generation information-rich user interfaces through gesture analysis.
Student roles: The student will be assigned a specialized role based on their background and current project demands. Depending on the assigned focus area, responsibilities will include one or more of the following:
• Custom ASIC Development: Developing new analog or digital components for our custom image sensor ICs. • Embedded Systems & Hardware: Working with custom camera PCBs equipped with high-performance FPGAs, memory chips, USB, and Ethernet interfaces. This includes programming parts of the FPGA using Verilog HDL for camera control and ML/CV inference acceleration, and setting up board-level peripherals. • Software & Interface Integration: Controlling the high-speed USB and Ethernet data links between the PCB and a host PC, and developing the software interface on the PC for reliable data acquisition. • Optics & Semiconductors: Characterizing novel 3D semiconductor photodetectors and/or designing optical testbenches. • Computer Vision & Computational Imaging: Designing, developing, and deploying computer vision and computational imaging algorithms and models.
The student will receive direct supervision and guidance from two Ph.D. students within the Intelligent Sensory Microsystems Lab.
Skills required: Ideal candidates possess these core qualifications: • Good knowledge of electronics or software development • Programming proficiency (C/C++/Python) • Self-driven interest in CMOS image sensors and computational imaging
Candidates with at least one of the following preferred skills are highly encouraged to apply: • Proficiency in HDL (Verilog) and digital ASIC/FPGA design (experience in ML model deployment on FPGAs is a plus) • Experience in analog circuit layout using Cadence Virtuoso • Background in optics or semiconductor device characterization • Experience with computer vision and computational imaging algorithms and models
10. Design of a Low-Noise Transimpedance Amplifier for Quantum Photonic Systems
Quantum photonic systems rely on highly sensitive optical detectors to measure extremely weak optical signals. The electronic interface between the photonic detector and subsequent signal-processing circuitry plays a critical role in determining the overall system performance. In particular, transimpedance amplifiers (TIAs) are widely used to convert small detector currents into measurable voltage signals while maintaining low noise and high sensitivity.
During this internship, the student will contribute to the design and simulation of a low-noise transimpedance amplifier intended for quantum photonic applications. The project will focus on the analysis of design trade-offs involving gain, bandwidth, noise performance, power consumption, and stability. The student will develop schematic-level circuit implementations, perform simulations, evaluate circuit performance against design specifications, and investigate optimization strategies to improve sensitivity and efficiency.
The internship will provide hands-on experience in analog integrated circuit design, circuit simulation, noise analysis, and photonic system applications. Through close interaction with graduate researchers and faculty members, the student will gain practical experience in designing electronic interfaces for emerging quantum photonic technologies.
Research area, student roles & skills
Research area: My research focuses on analog and mixed-signal integrated circuits for emerging photonic and quantum technologies. The work combines integrated circuit design, semiconductor device modeling, and electronic-photonic co-design methodologies to enable next-generation computing, communication, and sensing systems. Current research activities include electronic interfaces for silicon photonic integrated circuits, photonic neuromorphic processors, high-speed mixed-signal circuits, and integrated circuits for quantum photonic applications. The overall objective is to develop advanced electronic systems that enhance the performance, scalability, and energy efficiency of photonic and quantum technologies.
Student roles: The student will work under the supervision of graduate researchers and the principal investigator to support the design and evaluation of a transimpedance amplifier for quantum photonic applications. Responsibilities may include: - Reviewing the performance requirements of electronic interfaces used in quantum photonic systems. - Developing schematic-level implementations of a low-noise transimpedance amplifier. - Performing circuit simulations to evaluate gain, bandwidth, noise, stability, and power consumption. - Investigating design trade-offs and proposing circuit improvements. - Comparing alternative circuit topologies and documenting their performance. - Preparing technical reports and presentations summarizing the design methodology and simulation results. - Participating in technical meetings and research discussions with the project team. By the end of the internship, the student is expected to deliver a validated circuit design meeting the targeted specifications and a comprehensive report documenting the design methodology, simulation results, and recommendations for future development.
Skills required: The student should be enrolled in Electrical Engineering, Computer Engineering, Engineering Physics, or a related discipline and have prior hands-on experience in analog integrated circuit design. The candidate must possess knowledge of CMOS analog circuit design, semiconductor devices, transistor-level analysis, and analog simulation techniques. Experience using Cadence Virtuoso for schematic entry, DC/AC/transient simulations, and circuit verification is required. Previous coursework or project experience involving the design of analog integrated circuits is strongly preferred. The student should demonstrate strong analytical and problem-solving skills and have a keen interest in integrated circuit design for photonic and quantum technologies.
11. Design of an Integrated Hybrid Energy System for Waste Treatment Facility
Supervisor: Hossam Gaber
University: Ontario Tech University (Oshawa campus)
Location: Oshawa, Ontario
Start date: 2027-05-03 (flexible)
Disciplines: Electronic Systems, Engg-Mechanical, Engg-Systems and Technology, Engineering
This project is aiming to designing an integrated hybrid energy system with renewable energy technologies and storage systems as part of waste-to-energy process technology. The design will include control system and energy management system to achieve optimum energy use during the operation of WTE facilities. Energy system design with different renewable technologies and energy storage will be studied and integrated with WTE process. AI will be used for control design and optimization.
Research area, student roles & skills
Research area: Climate changes initiatives identified waste conversion to energy process technologies as a clean solution to overcome challenges of conventional waste treatment. The waste-to-energy (WTE) processes require high energy supply which made it challenging to be a profitable and viable solution. The use of renewable energy technologies could overcome this challenge by introducing hybrid energy system to cover high energy needs and achieve reduced cost of WTE process technologies.
Student roles: work with team to analyze energy system and design interface within waste treatment facility, with control design and data analysis
Skills required: The proposed solution includes the following deliverables: - Analysis of energy demand of waste-to-energy process - Design of hybrid energy system with storage systems as integrated with the waste-to-energy - Control design of the integrated waste-to-energy with hybrid energy system - Prototype system and demonstration of the WTE process with control system
12. Design of drone-based inspection system for industrial applications
Supervisor: Hossam Gaber
University: Ontario Tech University (Oshawa campus)
Location: Oshawa, Ontario
Start date: 2027-05-03 (flexible)
Disciplines: Electronic Systems, Engg-Industrial, Engg-Mechanical, Engg-Software, Engg-Systems and Technology, Engineering
This project is aiming to designing Conceptual and detailed design, as well as actual build of the prototype system of drone-based inspection system for industrial facilities.
Research area, student roles & skills
Research area: There are number of challenges and limitations of inspection activities in industrial facilities, including hazards to human inspection, long time of repeated inspection tasks, and difficulties of inspection tools to reach different physical locations. Drones are widely used for number of industrial applications. The use of drones for plant inspection requires investigation and provide engineering design to integrate properly and safely within nuclear power plant maintenance and inspection activities.
Student roles: The following are the main deliverables: - Define design requirements, constraints, challenges of inspection activities in industrial facilities, and identify challenges and select tasks for drone-based inspection - Study inspection standards and codes related to inspection and maintenance and use of robotics and drones in industrial facilities - Conceptual and detailed design of drone-based inspection - Drone technology assessment in view of inspection activities - Assess risks associated with inspection activities and the use of the proposed solution - Design integrated monitoring system with data analysis - Planning and management of drone operation based on mission planning, drone condition (including energy management), and measured data
Skills required: drone, robotics, mechatronics, AI, Python, control design
13. Development and implementation of an algorithm for polygon manipulation to be used in a tool for cleaning design layouts.
Supervisor: Ahmed Lakhssassi
University: Université du Québec en Outaouais (Gatineau campus)
The purpose of this project is to develop and implement an algorithm that manipulates polygons coordinates to meet a set of specific drawing rules. Based on the concepts of multithreading in Java and the backtracking, the algorithm will have to be able to find the new coordinates of the polygons to correct an error locally while making sure that it will not generate another elsewhere.
Research area, student roles & skills
Research area: Object-Oriented Design, multithreading, backtracking algorithm, Shell, syntactic analysis
Student roles: - Develop high performance software. - Understand and maintenance of existing code. - Develop high performance software. - Leverage technology to solve challenging problems posed by Product Owners. - Drive optimizations in the existing product.
The project includes the investigation into various biosensors and transducers. These devices leverage the benefits of micro and nano fabrication technology, MEMS, and advanced material and chemical engineering to develop a robust, reliable, and miniaturized smart sensor systems. The sensor is used to detect variation biomarkers or compounds. The sensor and transducers properties and sensitivity are highly dependent on the sensor geometry, dimension, material and method of detection of the electrical signals. Therefore, a careful design and simulation of the sensor configuration is often needed. In this project, students investigate various sensor and transducer design and method of detection. They will conduct FEM simulations (COMSOL) to design and investigate the performance of each sensor and transducer under various conditions. Students will also investigate various bio-compatible and sensitive materials that can be used in medical and environmental sensor systems.
Research area, student roles & skills
Research area: Dr. Arezoo Emadi is the director of electrical Micro and Nano Devices and Sensors (e-Minds) Research Centre, www.emadilab.com. She leads academic and industry cross-functional projects to introduce and implement advanced sensor technologies and micro electro mechanical systems (MEMS) technologies in a wide range of fields that make abundant use of sensors and transducers such as medical and environmental sciences. Her ongoing research efforts are in the area of micro and nano electronic devices and integrated sensors, chemical and medical sensors, BioMEMS, state-of-the-art ultrasonic transducers, and medical imaging systems and micro and nano fabrication processes.
Student roles: Students will have the following role and responsibility in their relevant part of the project: Conducting literature review, summarizing available sensor technologies and investigating the evolutionary and state-of-the-art sensors through reading journal, articles and conference papers; designing various sensors and transducers (including QCM, PMUT, CMUT) suitable for medical diagnostic applications as well as environmental monitoring; conducting FEM simulations such as COMSOL as well as modelling the sensor and optimizing the system based on the simulation results; investigating suitable structural material for each configuration and investigating method of material deposition; preparing sensor fabrication layout, test and characterization of sensor; writing report and presenting their findings to the group members; collaborating, communicating and assisting other team members.
Skills required: The project is suitable for students with a background in electrical engineering and interest in bioengineering, material and chemical engineering. The project requires study of various sensor and transducer configuration an investigation into structural materials of the sensor, method of detection, and design and simulation of micromachined sensors. Familiarity with FEM and COMSOL simulation software is an asset. Knowledge and interest in MEMS sensor system, as well as basic knowledge of micromachining, would be an asset.
15. Development of Electronic Piezo and Capacitive Micromachined Ultrasonic Transducer, PMUT
The project includes the investigation into micromachined ultrasonic transducers and in particular piezo-based as well as capacitive based transducers for medical imaging applications, also anemometer. The focus is on development of high-power, high-frequency, and high sensitivity transducer, method of fabrication, packaging and potential integration in portable electronics. Contact mode and coupled transducers will be investigated and matching layers, structural materials and configuration as well as transducer performance will be optimized. Students will conduct FEM simulations (e.g. COMSOL) to design and investigate the performance of transducers under various conditions.
Research area, student roles & skills
Research area: Dr. Arezoo Emadi is the director of electrical Micro and Nano Devices and Sensors (e-Minds) Research Centre, www.emadilab.com. She leads academic and industry cross-functional projects to introduce and implement advanced sensor technologies and micro electro mechanical systems (MEMS) technologies in a wide range of fields that make abundant use of sensors and transducers such as medical and environmental sciences. Her ongoing research efforts are in the area of micro and nano electronic devices and integrated sensors, chemical and medical sensors, BioMEMS, state-of-the-art ultrasonic transducers, and medical imaging systems and micro and nano fabrication processes.
Student roles: Students will have the following role and responsibility in their relevant part of the project: Conducting literature review, summarizing available transducer technologies and investigating the evolutionary and state-of-the-art ultrasonic imaging system through reading journal, articles and conference papers; designing MEMS-based and high-frequency piezo transducer; conducting FEM simulations such as COMSOL to design the transducer as well as modelling the transducer and optimizing the system based on the simulation results; investigating suitable structural materials and piezo material for each configuration; preparing fabrication layout; and test and characterization of various transducers; writing report and presenting their findings to the group members; collaborating, communicating and assisting other team members.
Skills required: The project has different components suitable for students with a background in electrical engineering and and mechanical engineering with interest in biomedical engineering. The project requires study of various MEMS transducer configuration and design and simulation of micromachined sensors. Familiarity with FEM and COMSOL simulation software is an asset. Knowledge and interest in MEMS system, as well as basic knowledge of micromachining, would be an asset.
16. Development of Robotic Self-Replicating Machine for the Moon
This project combines in situ resource utilisation (ISRU) with 3D printing technology for eventual deployment on the Moon. We are working on developing a robotic self-replicating machine that can mine lunar regolith, extract its required materials thermochemically, and input them into a series of 3D printers to construct parts of itself. These parts may be assembled into full systems. If such a self-replicating machine can be developed, it would revolutionise space exploration, opening up possibilities hitherto considered too difficult or too expensive to undertake. This is a very challenging project requiring the full spectrum of engineering skills - aerospace, mining, chemical, manufacturing, mechanical, electronic, software, etc. It is also a very exciting and unique project.
Research area, student roles & skills
Research area: My specialty is in space robotics (I am a Canada Research Chair in this field) crossed trained as a spacecraft engineer and robotics/mechatronics and a healthy dose of planetary science. My traditional areas of research are in space manipulator control, planetary rover traction and navigation, robotic vision, planetary drill design, biomimetics and astrobiology science. I expect my graduate students to become familiar with these areas. Most of my recent work has been in planetary rovers in developing enhanced rover intelligence to support planetary missions.
Student roles: There are several options for roles but they will all be supporting the larger project with graduate students. Most of the student roles will involve physical design and construction: (i) design and construction of 3D printer prototypes (ii) design and construction of motor and electronic circuits (iii) design of software modules Roles will be assigned according to our needs.
Skills required: Machine shop skills (eg. lathe, 3D printing, milling, etc) Software skills (eg. Matlab - Simulink - Labview - CAD - Multibody Dynamics - Stress Analysis) Programming skills (eg. C/C++) Analytical skills with good engineering judgement Self-starter with initiative Enthusiasm is essential Good work ethic
17. Digital Fingerprints for Secure Internet of Things
The growing cybersecurity threats to networked electronics, such as Internet of Things (IoT), call attention to the need for robust and convenient hardware-based authentication methods. Given that transistors inherently exhibit fabrication-originated variations, forming the foundation of physically unclonable function (PUF) characteristics, every electronic circuit containing transistors can, in principle, function as a PUF.
Microcontrollers, which typically include static random-access memory (SRAM), offer a practical platform for implementing such security mechanisms. This study investigates the use of SRAM as a source of PUFs, leveraging the unique and reproducible digital "fingerprint" generated during the initial power-up sequence. Experiments will be conducted using STM and Arduino development boards to demonstrate the feasibility and reliability of SRAM-based authentication methods commonly found in microcontroller units.
SRAM PUFs exploit intrinsic manufacturing randomness in SRAM cells to generate unique digital keys, which can be used both for authenticating hardware devices and for cryptographic applications. Upon power-up, SRAM cells settle into either a 0 or 1 state based on mismatches between transistors. These inherent and stable variations create a consistent yet unpredictable key each time the device is powered on. As this key is never stored and only arises at startup, it provides an added layer of security. The outcome of this research is to characterize the fundamental properties of SRAM-based PUFs and their potential to enhance the security of low-power IoT systems.
Research area, student roles & skills
Research area: Professor Gregori's research focuses on integrated microsystems, including low-voltage, low-power circuits, energy harvesters, microsensors, and integrated circuits for sensor networks and wearables. He combines theoretical and experimental aspects and explores real-world applications. He is the head of the Microelectronics Laboratory at the University of Guelph, which includes a computing facility for modelling, simulating, and designing microsystems and a measurement facility for testing and characterizing integrated devices and circuits, from direct-current to GHz-range frequencies. The laboratory is affiliated with CMC Microsystems, which provides integrated circuit fabrication capacity and access to specialized facilities.
Student roles: The student will be responsible for programming microcontroller development boards (STM and Arduino) using C, Python, and Assembly. The objective of the code will be to read the initial SRAM-generated contents upon power-up. This task will involve configuring the development boards in a master/slave setup, accessing the bootloader to prevent SRAM data from being overwritten, and transferring the SRAM data to a computer for further analysis. The hardware setup will include components such as protection resistors, indicator LEDs, and relays, assembled on a breadboard or a soldered circuit. Familiarity with tools such as MS Visio and Inkscape is useful, as the student will be expected to produce high-quality technical diagrams for documentation. In addition to hardware and software development, the student will contribute to the creation of statistical code for analyzing the collected SRAM data. The role also involves close collaboration with the project supervisor, researchers and fellow students. The student will share research outcomes through regular progress presentations and will be required to write a comprehensive technical report upon completion of the project. This project offers a valuable opportunity to gain new technical skills and deepen subject-matter expertise. It is designed to foster originality, critical thinking, and innovation in the context of applied research.
Skills required: The student should have a solid background in microcontroller programming and experience in connecting electronic components into a circuit using breadboards. A good understanding of circuit analysis techniques, characteristics and applications of electronic devices and integrated circuits, as well as familiarity with electronic circuit simulation tools and laboratory instrumentation, is required. These competencies are typically developed through undergraduate coursework in electronic circuits, signal processing, systems and control, digital systems, and computer programming. This research project involves programming microcontroller development boards. Therefore, prior experience in programming microcontrollers is required, and completion of a course in embedded systems would be an asset.
18. Développement d'un robot mobile autonome muni de LIDAR de vision et intelligence artificiele
Supervisor: MOHSEN GHRIBI
University: Université de Moncton
Location: Moncton, New Brunswick
Start date: 2027-05-10 (flexible)
Disciplines: Electronic Systems, Engg-Systems and Technology, Engg-Computer
Le développement d'un robot mobile muni d'intelligence artificielle suscite un intérêt grandissant pour répondre à des exigences industrielles de plus en plus complexes et sévères. Le robot envisagé pose beaucoup de défis technologiques qui sont de nature multidisciplinaire: électronique industrielle et commande des systèmes et entrainement à vitesse variable des moteurs, électronique et capteurs, développement d’algorithmes complexes, programmation de mouvement, et cartographie à l'intérieurs de batiments.
Ce projet vise donc à mettre l’accent à l’étude et au développement de la commande d’une plateforme mobile autonome, capable de naviguer avec précision dans un environnement dynamique et complexe tout en identifiant et en évitant les obstacles (humains et objets). La précision du déplacement se pose comme critère important pour assurer une utilisation future avec un robot manipulateur qui doit exécuter des tâches avec une flexibilité accrue selon les besoins les besoins des processus industriels complexes.
La plateforme mobile munie de quatre roues doit être équipée d’un système de navigation et d’un système de détection et d’évitement d’obstacles qui assurent son déplacement en toute sécurité. Les roues sont entrainées à vitesse variable par des moteurs alimentées par des convertisseurs statiques et les capteurs appropriés pour l’entrainement électronique. Une carte à microcontrôleur assure le fonctionnement de la plateforme en utilisant les données fournies des capteurs et détecteurs de navigation à savoir un LIDAR, caméra et capteurs à ultrason). La conception et le choix des composants s'imposent en premier pour répondre à un cahire des charges et des besoins d'utilisation spécifiques.
Le développement sera par la suite porté au développement du modèle mathématique représentant le comportement dynamique de la plateforme mobile. Le modèle servira pour ensuite développer et évaluer des stratégies de commande qui assurent le déplacement dans l’environnement d’utilisation sous des vitesses et trajectoire optimales tout en évitant les obstacles dynamiques.
Research area, student roles & skills
Research area: Électronique de puissance, électronique, les microcontrôleurs, commande des systèmes. Mes réalisations sont dans la commande adaptative des moteurs synchrones à aimant, la commande optimale appliquées aux moteurs CC et CA, le développement de convertisseurs statiques et des applications avec microcontrôleurs. Ces dernières années l’accent est mis sur l’Internet des objets (IdO) et robotique mobile. Mes travaux actuels : la minimisation de la consommation énergétique dans les moteurs CA et le chauffage électrique résidentiel. J'ai travaillé sur plusieurs projets de développement pour l'industrie (robotique industrielle, IoT, capteurs,électronique) et j'ai supervisé un nombre considérable d’étudiant(e)s des différents cycles et des des stagiaires.
Student roles: L’étude et le développement doit tenir compte de l’hypothèse que la plateforme sera utilisée dans une étape ultérieure future à transporter un robot manipulateur pour faciliter l’exécution de tâches dans un environnement sous changement continuel. La plateforme sera toutefois de taille petite pour mettre principalement l’accent sur le développement des stratégies de commande et de navigation et la détection des obstacles et la cartographie intérieure. Elle sera fournie mais nécessite quelques ajouts et intégrations de divers capteurs et cartes électroniques et composants essentiels tel que le LIDAR, Caméra et cartes NVIDIA, ou Raspberry PI.
- Étude bibliographique sur les robots mobiles munis de 4 roues - La modélisation dynamique de la plateforme mobile et identifier ses divers paramètres, - Valider le modèle obtenu par simulation et par expérimentation, - Développer des lois de commande pour le déplacement et la navigation sous des trajectoires simples et complexes sans la présence d’obstacles, - Développer les codes pour microcontrôleurs et valider le fonctionnement adéquat des lois de commande développées. Les techniques de commande seront considérées et comparées sous diverses stratégies de déplacement et de détection. Une carte facilitant l'intégration de plusieurs capteurs sera dévelopée et intégrées dans la plateforme à utiliser. - Développer les fonctions de détection d’obstacles et leur évitement en utilisant diverses techniques de détection avec un LIDAR, camérar et des capteurs à ultrason, - Documenter les étapes du travail accompli et fournir un rapport et les codes de simulation et de validation expérimentale
Skills required: - Notions sur la modélisation et l'identification des systèmes dynamiques - Compréhension des systèmes asservis numériques et conception de régulateurs, - représentation par les variables d'état - Simulation sur ordinateur des systèmes dynamiques - Utilisation de Matlab, langages de programmation - Familier avec les microcontrôleurs et leur programmation. - entrainement des moteurs CC, Circuits électroniques - Développement de cartes électroniques, utilisation des capteurs, camera, notion de IA et vision - L'étudiant doit être familier avec le logiciel MSProject (ou tout autre logiciel équivalent).
19. Développement et Conception des Capteurs Quantiques de Température pour Applications Avancées
This project aims to revolutionize thermal sensing by designing ultra-precise quantum sensors tailored to the demands of advanced technologies such as quantum electronics, semiconductor manufacturing, and critical infrastructure monitoring. By leveraging quantum phenomena like superposition and entanglement, it overcomes the limitations of conventional sensors in sensitivity and stability. The approach focuses on nitrogen-vacancy (NV) centers in diamond for nanometric optical detection and superconducting circuits for extreme thermal sensitivity. This innovation paves the way for next-generation metrology, enabling reliable, high-resolution thermal measurements in demanding environments and unlocking new possibilities across scientific and industrial applications.
Research area, student roles & skills
Research area: My research focuses on the development of advanced quantum sensors and their integration into intelligent systems for real-time monitoring of critical infrastructures. By combining principles of quantum physics, materials engineering, and the Internet of Things (IoT), my work aims to design ultra-sensitive detection devices capable of operating in complex environments. These technologies are particularly relevant for applications in sustainable transportation, advanced electronics, and hardware-based cybersecurity.
Student roles: The student will contribute to the design, modeling, and characterization of quantum sensors. They will be responsible for conducting numerical simulations, analyzing experimental data, and participating in the integration of sensors into an IoT platform. They will play an active role in writing reports, preparing scientific publications, and presenting results. The work will take place in a multidisciplinary environment, in collaboration with experts in physics and electrical engineering.
Skills required: The ideal candidate should have a strong background in applied physics, electrical engineering, or nanotechnology, with knowledge of quantum mechanics, electronic instrumentation, and/or materials science. Skills in scientific programming (e.g., Python, MATLAB), numerical modeling (e.g., COMSOL, DFT), or experimental data analysis are highly valued. Experience with quantum devices, sensors, or IoT integration is an asset. The student must demonstrate scientific rigor, curiosity, and the ability to work both independently and within a multidisciplinary research environment.
20. Energy monitoring for renewable energy system
Supervisor: Adel Merabet
University: St. Mary's University (Halifax campus)
This project focuses on energy monitoring and control of renewable energy-based microgrids. It involves real-time tracking of power generation, storage, and consumption from sources such as solar PV and wind, as well as the development of control strategies for power electronic converters to maintain voltage stability and balance energy flow.
The student will analyze system data, implement monitoring tools, and support the design of control and optimization algorithms to improve microgrid performance and efficiency. MATLAB, Python, or C will be used for modeling, simulation, and data analysis.
Research area, student roles & skills
Research area: The Smart Sustainable Systems and Automation Lab (S³A Lab) is a research facility within the Division of Engineering at Saint Mary’s University. The lab is dedicated to advancing mechatronics, autonomous control, and sustainable energy technologies. By integrating Artificial Intelligence (AI) and the Internet of Things (IoT) with traditional engineering disciplines, the S³A Lab develops innovative solutions for the complex challenges of modern energy management and industrial automation.
Student roles: The student will support the development of a renewable energy microgrid monitoring and control system by analyzing system data, implementing monitoring tools, and assisting in the design and testing of control strategies for power electronic converters to improve voltage stability, power sharing, and overall system performance.
Skills required: Required Skills/Background of the Student - Undergraduate student in Electrical Engineering, or related field - Basic programming skills in MATLAB, Python, or C - Understanding of basic circuits, power systems, or renewable energy systems (asset) - Interest in control systems, power electronics, and data analysis - Familiarity with modeling, simulation, or optimization techniques (asset) - Strong analytical and problem-solving skills
21. Exploration of Quantum Devices with Emerging Chiral Nanomaterials
Supervisor: Sandipan Pramanik
University: University of Alberta (Edmonton campus)
Solid-state electronics has played a key role in shaping today’s information-based society. Since the invention of the transistors in the late 1940s, electronics industry has enjoyed phenomenal growth and has fundamentally transformed the way we work, communicate, entertain and think. So far, silicon has been the key material for a vast majority of electronic devices. However, non-conventional materials and device concepts may play a key role in future electronics. Of particular interest are "carbon based" and "two-dimensional (2D)" nanomaterials, such as graphene, carbon nanotubes, the entire family of metal dichalcogenides and many others. Graphene is a strictly two-dimensional material in which carbon atoms are arranged in a planar honeycomb lattice. Graphene exhibits high carrier mobility, in which charge carriers behave like massless particles and may allow low power operation. Being strictly two-dimensional, quantum mechanical effects often manifest in graphene even at room temperatures. Carbon nanotubes can be viewed as rolled up graphene sheet. In recent times several 2D materials have been shown to exhibit magnetism, which may propel magnetic field sensing and data storage technology to its ultimate scaling limit. In addition, introduction of structural chirality in these materials (either intrinsically or via some extrinsic agent) can provide additional control on the spin polarization of the charge carriers, with immense implications for the field of spintronics (spin-based electronics). Such exciting properties of these materials and their immense potential in diverse sectors such as quantum computing, communications, quantum sensors etc. motivate our research in this area.
With the above background in mind, the aim of this project is to explore and understand various emerging properties of these novel materials by experimental techniques. The candidate will primarily engage in experimental activities such as nanofabrication, characterization and measurement of electronic/spintronic device properties. These activities will take place in the experimental facilities mentioned above.
Research area, student roles & skills
Research area: Our research at the University of Alberta explores emerging chiral nanoelectronic devices that exploit the quantum mechanical effects at nanoscale geometry. Over the past several years we have worked on chiral carbon nanotubes and graphene devices, which show "chirality-induced spin selectivity" (CISS) effect, with implications for the area of spintronics (or, spin-based electronics). Further details of our work can be found from our recent papers. We closely work with the in-house nanoFab (a nanofabrication facility), on-campus National Research Council (NRC) and various other local experimental facilities. Device characterization and measurements are performed in our own custom-built setup.
Student roles: (1) Performing thorough literature review using various research databases available at the University of Alberta library (2) Developing a research plan in consultation with the group members (3) Analysis and interpretation of experimental results (4) Participation in group meetings and presentation of results (5) Assist in preparation of manuscripts for publications in journals and/or conference proceedings
Skills required: (1) Experience in experimental nanotechnology such as nanofabrication will be helpful, but not mandatory (2) Experience in LabView programming, machine learning (ML) (3) Basic knowledge of graphene, carbon nanotubes and emerging 2D materials (4) Knowledge of basic quantum mechanics (Schrodinger equation), solid-state physics (crystal structure, Bloch’s theorem, band structure calculation etc.) (5) Basic knowledge of electronic devices such as diodes and transistors (BJT and MOSFET). Understanding at typical 3rd year undergraduate level is expected.
22. FPGA-Based Exploration of Deterministic RISC-V Microarchitecture Features
This project aims to explore how open RISC-V processor architectures can be adapted to support deterministic execution in safety-critical embedded systems. Determinism is a key requirement in domains such as aerospace, where systems must exhibit predictable timing behavior to ensure reliability and facilitate certification.
The project will focus on implementing and evaluating a simplified RISC-V-based system on an FPGA platform. The intern will investigate hardware mechanisms that influence timing predictability, including memory organization (e.g., cache versus scratchpad memory), basic bus arbitration strategies, and lightweight timing monitoring features. These mechanisms will be integrated into an existing open-source RISC-V core or a minimal custom design.
Using FPGA prototyping tools, the intern will deploy the system and run controlled workloads to measure execution time variability. The goal is to better understand how architectural choices impact timing behavior and to identify configurations that improve predictability while maintaining reasonable performance.
The project combines hardware design, experimental evaluation, and data analysis. It is designed to be accessible while providing exposure to advanced topics in computer architecture, real-time systems, and embedded computing. The intern will also gain hands-on experience with FPGA development tools and open-source hardware ecosystems.
This work contributes to the broader objective of developing transparent and certifiable computing platforms based on open architectures. By improving our understanding of deterministic hardware design, the project supports ongoing research in safety-critical systems and next-generation avionics platforms.
Research area, student roles & skills
Research area: My research focuses on deterministic embedded systems and FPGA-based computing architectures for safety-critical applications. In particular, I investigate how open RISC-V processors can be adapted to provide predictable timing behavior required in domains such as aerospace. This includes the design of hardware mechanisms to reduce execution variability, such as memory architectures (caches vs scratchpads), bus arbitration, and timing monitoring. FPGA prototyping is used to validate these concepts in realistic conditions. The goal is to enable certifiable, transparent, and efficient computing platforms that meet the strict requirements of real-time and safety-critical systems.
Student roles: The student will play an active role in the design, implementation, and evaluation of a deterministic RISC-V-based system on FPGA. Under the supervision of the research team, the student will contribute to the development of a simplified hardware platform and will participate in all stages of the experimental workflow.
The student will begin by familiarizing themselves with the development environment, including FPGA tools and the selected RISC-V core. They will then assist in implementing specific hardware features aimed at improving timing predictability, such as memory configurations (e.g., cache or scratchpad) or simple monitoring mechanisms. This phase may involve modifying existing designs or integrating predefined modules.
Once the system is operational, the student will be responsible for designing and running experiments to evaluate execution time variability under different configurations. This includes preparing test programs, collecting data, and analyzing results using appropriate tools (e.g., Python or MATLAB). The student will be encouraged to interpret the results and identify key insights related to system behavior.
Throughout the internship, the student will participate in regular meetings, document their work, and present progress updates. They will also contribute to the preparation of a final technical report summarizing their methodology, results, and conclusions.
This role provides a balance between guided learning and autonomy, allowing the student to develop practical skills in FPGA prototyping, embedded systems, and experimental research. The student is expected to demonstrate initiative, curiosity, and the ability to work both independently and as part of a research team.
Skills required: The ideal candidate has a background in electrical engineering, computer engineering, or computer science. Basic knowledge of digital design, computer architecture, or embedded systems is expected. Familiarity with programming in C/C++ and experience with hardware description languages (e.g., Verilog or VHDL) are assets. An interest in FPGA-based development and real-time systems is important. The student should be comfortable working in a Linux environment and willing to learn new tools and concepts. Strong analytical skills, autonomy, and curiosity are essential for successfully completing the project.
23. Integrated Circuits for timing applications
Supervisor: Frederic Nabki
University: École de Technologie Supérieure (Montréal campus)
Location: Montreal, Québec
Start date: 2027-05-03 (flexible)
Disciplines: Electronic Systems, Engg-Electrical, Engg-Electrical, Engineering, Studies Science and Technology
Clock signals are the heartbeat of electronics. Every electronic device needs a clock, just as every human being needs a heart.
Historically, these clock signals have always been produced using quartz oscillators.
In this project, we propose to explore the use of micro-electro-mechanical systems (MEMS) resonators to create clock signals. Oscillators are an essential component of all devices, from the automotive industry to the space industry.
The main objective of this project is to create a MEMS resonator evaluation platform capable of testing MEMS resonators with operating frequencies between 1MHz and 200MHz. If the frequency range is too wide, it can be reduced to between 10MHz and 100MHz.
The entire evaluation platform should be built using off-the-shelf components and measuring instruments available at the ÉTS.
The MEMS resonator evaluation platform should be able to provide the following information:
MEMS resonator frequency.
MEMS resonator frequency stability over time.
MEMS resonator temperature stability.
MEMS resonator phase noise.
MEMS resonator jitter.
Power consumption of the entire system.
There are 4 deliverables for this project:
A MEMS resonator conditioning system.
A MEMS resonator temperature control system.
A MEMS resonator performance measurement system.
A report and user manual for the system.
Research area, student roles & skills
Research area: My research interests are mixed-signal and radiofrequency integrated circuits and microelectromechanical systems (MEMS) for various applications including sensing and communications. Some research projects include the design of novel low-power and high speed analog-to-digital converters, CMOS phase-locked loops, ultra-wideband transceivers, and wired communication channels. In addition, I am active in research projects involving the creation of next generation MEMS fabrication processes using advanced materials, the integration of MEMS devices with CMOS systems, and the modeling of MEMS devices.
Student roles: Selon son expérience, le stagiaire sera impliqué dans la conception de la plateforme d'évaluation . La conception se fera à l'aide d'outils de conception assistée par ordinateur commerciaux tels que Altium Designer, Kicad, LT spice. Le stagiaire sera pris en charge par les étudiants gradués et un chercheur postdoctoral. Le stagiaire peut également être impliqué dans les tests de laboratoire au niveau du système, dans la modélisation et dans les simulations.
Skills required: The students should have taken basic courses in the field of analog electronics design. Knowledge of how to design, simulate electronics components is an asset. Knowledge of integrated circuits design is an asset. Knowledge of PCB design is an asset.
24. Integrated Power Converters for Insect-Scale Robots
In disaster recovery operations and high-risk environments, the deployment of insect-scale robotic platforms presents a promising approach to data acquisition where physical manipulation is not required. These microrobots, lightweight, mobile, and minimally invasive, excel in tasks requiring wide-area coverage and real-time sensing. However, their limited size, weight, and power capacity significantly constrain autonomous operation, with energy management being the most critical challenge.
This project addresses the need for energy autonomy by developing and simulating integrated, ultra-low-power power management circuits. Designed for highly variable energy sources such as ambient light, these circuits will be fully monolithic and optimized for the stringent constraints of microrobotic systems.
Key research areas include energy harvesting and reconfigurable power topologies. The performance will be validated through simulation, embedded program code will be optimized to reduce computation overhead and minimize energy consumption, and a demonstrator for a target application will be developed. The expected outcome is a set of efficient, validated circuit designs that extend operational lifetime and support the deployment of microrobots in real-world scenarios. Results will be documented and shared through peer-reviewed publications or conference presentations to promote further innovation in related fields.
Research area, student roles & skills
Research area: Professor Gregori's research focuses on integrated microsystems including low-voltage low-power circuits, energy harvesters, microsensors, and integrated circuits for sensor networks and wearables. He combines theoretical and experimental aspects and explores real-world applications. He is the head of the Microelectronics Laboratory at the University of Guelph, which includes a computing facility for modelling, simulating, and designing microsystems, and a measurement facility for testing and characterizing integrated devices and circuits, from direct-current to GHz-range frequencies. The laboratory is affiliated with CMC Microsystems that provides integrated-circuit fabrication capacity and access to specialized facilities.
Student roles: The students will apply electronic-design-automation tools to design, verify, and improve circuits and systems. They will define building blocks and modify and interconnect them for obtaining given functions. They will learn how to identify critical points and estimate performance constraints. The project will not only provide an opportunity to acquire new technical knowledge and practical skills, but also encourage originality and innovation in the application of that knowledge. Students will regularly present their progress, sharing research outcomes and receiving feedback. Their role will also include close collaboration with the supervisor, peers, and other researchers, active participation in the dissemination of results, and the preparation of a comprehensive technical report by the end of the period.
Skills required: The students contributing to this project will have a solid background in electronics. They will have a good understanding of the mathematical tools for describing continuous-time and discrete-time systems, the techniques for circuit analysis, the main characteristics and applications of electronic devices and integrated circuits, the concepts for programming with a hardware description language, the tools for simulating electronic circuits, and the typical laboratory electronic instrumentation. These topics are covered by undergraduate courses about circuits, electronics, signal processing, control, and digital systems.
25. Low-Power High Throughput Air-Water Networking
This project aims to develop a next-generation low-power, high-throughput communication framework for seamless networking across air and water environments using advanced metasurfaces. The research addresses the significant challenges associated with signal propagation through the air-water interface, including reflection losses, refraction, scattering, and attenuation, which limit the performance of conventional communication systems. Potential applications include autonomous underwater vehicle (AUV) communications, ocean monitoring networks, offshore infrastructure inspection, environmental sensing, maritime surveillance, and underwater Internet of Things (IoUT) systems. The expected outcome is a scalable and sustainable communication platform that significantly improves the performance and energy efficiency of cross-medium wireless networks.
Research area, student roles & skills
Research area: My research focuses on the algorithmic design, optimization, and performance analysis of wireless communication networks, with particular interest in multi-antenna systems (MIMO), active and passive reconfigurable intelligent surfaces, integrated sensing and communication systems, millimeter-wave and terahertz communications, and the application of machine learning to next-generation wireless communication systems. My work aims to advance intelligent, efficient, reliable, and physically consistent wireless communication systems through the integration of information theory, machine learning, and electromagnetic-aware communication design, helping shape the future of 6G and beyond networks
Student roles: -Conducting literature reviews on metasurfaces, wireless communications, underwater networking, and cross-medium communication systems. -Developing analytical models and simulation frameworks to evaluate wave propagation and communication performance in air-water environments. -Designing and optimizing metasurface structures to enhance signal transmission, beam steering, and energy efficiency. -Implementing and testing communication protocols and signal processing algorithms for reliable and high-throughput data exchange
Skills required: The student should possess knowledge in several of the following areas: -Electromagnetic theory and wave propagation. -Wireless communication systems and networking protocols. -Signal processing and communication theory. -Numerical modeling and simulation tools such as MATLAB, Python -optimization, and machine learning techniques (an asset).
26. Machine Learning-Enabled Adaptive Neurostimulation for Patient-Optimized Biomedical Prostheses
We are designing the world’s smartest and smallest neural prosthesis for bio-medical applications. The project aims to address unmet needs of intelligent closed-loop stimulation of a
nerve for artificially controlling a paralyzed body. The above figure shows a system diagram. The collaborative team is developing a novel chip and associated methods for detection of muscle movement through an adaptive nerve stimulation, while automatically learning the best possible combinations of excitation and inhibition paradigms. The method will immensely contribute to the area of neural prostheses and other related applications.
Students will participate in machine learning algorithm development, testing, system integration and some integrated circuit (IC) design. Publications in high standard journals and conferences are inevitable. The project will be beneficial for cutting-edge skill and knowledge enhancement for the associated students.
Research area, student roles & skills
Research area: Our research focuses on the design of cutting-edge integrated circuits (IC) for closed-loop neural interfacing systems. We develop efficient/implantable chips that can simultaneously record neural signals, analyze information, and deliver desired stimulation in real time. By integrating these features on a single chip, we hope to create an intelligent and adaptive system for biomedical applications. Our work spans multiple disciplines, including low-power analog/mixed-signal circuit designs, neural signal processing, wireless power/data transfer, bioelectronics, etc.
Student roles: The student will work with a team of talented engineers and researchers to develop a closed-loop system for muscle movement and adaptive stimulation. Student's work will include: • Designing PCBs for chip testing • Programming FPGAs or embedded systems for control • Developing machine learning algorithms • Participating in testing and experiments
Skills required: Background/affiliations in ECE/Eng.Sci./CS with experience/interests in any two or more points: • Machine learning related coding (Python, Verilog, etc.) • PCB-level circuit design and hardware testing • FPGA design and testing • Integrated-Circuit Design and Layout • Analog, digital, or radio frequency (RF) designs • (Very important) Enthusiastic, independent learner, and strong soft skills.
27. Micropower Converters for Batteryless Internet of Things
The objective of this project is to investigate switched-capacitor integrated circuits optimized for ultra-low-power, batteryless Internet of Things (IoT) applications. In such systems, conventional batteries are replaced by ambient energy harvesting sources such as thin-film solar cells or vibration-based microgenerators. These sources deliver intermittent and low levels of power, demanding highly efficient, adaptive circuits that can function reliably without battery support.
To address these challenges, the project focuses on the design and analysis of reconfigurable, fully integrated circuits that are not only compact and lightweight (essential for IoT miniaturization) but also capable of operating on minimal energy budgets. Special attention will be given to maximizing power conversion efficiency and minimizing quiescent power consumption, ensuring sustained operation under variable and weak energy input conditions.
Key circuit functions will include energy decoupling and regulation using high-frequency switching techniques, allowing the energy harvester to be decoupled from the load dynamically. Behavioural modelling and detailed circuit-level simulations will be employed to validate and optimize these functions. Innovative functional architectures will be developed using electronic-design-automation tools and CMOS integration technologies.
The effectiveness of the proposed solutions will be assessed through structural simulations, focusing on performance metrics critical to batteryless operation. The project's findings aim to advance the field of self-powered electronics and will be disseminated with the intent to support emerging applications in wearable devices, smart packaging, and autonomous environmental sensing.
Research area, student roles & skills
Research area: Professor Gregori's research focuses on integrated microsystems including low-voltage low-power circuits, energy harvesters, microsensors, and integrated circuits for sensor networks and wearables. He combines theoretical and experimental aspects and explores real-world applications. He is the head of the Microelectronics Laboratory at the University of Guelph, which includes a computing facility for modelling, simulating, and designing microsystems, and a measurement facility for testing and characterizing integrated devices and circuits, from direct-current to GHz-range frequencies. The laboratory is affiliated with CMC Microsystems that provides integrated-circuit fabrication capacity and access to specialized facilities.
Student roles: The students will apply electronic-design-automation tools (Cadence Virtuoso Studio) to design, verify, and improve circuits and systems. They will define functional building blocks, adapt and interconnect them to implement specific functions, and develop the ability to identify critical design challenges and estimate performance constraints. Beyond gaining technical knowledge and practical skills, the project will foster creativity and innovation in applying engineering principles to real-world problems. Students will also collaborate closely with the supervisor, fellow students, and researchers, contributing to the dissemination of results. The student will share research outcomes through regular progress presentations. At the conclusion of the project, they will document their work and findings in a comprehensive technical report.
Skills required: Students are expected to have a strong foundation in electronics, supported by a solid understanding of both theoretical and practical aspects. Their background will include mathematical tools for analyzing continuous-time and discrete-time systems, as well as techniques for circuit analysis and modelling. They will be familiar with the characteristics and applications of electronic devices and integrated circuits, and possess working knowledge of hardware description languages for digital design. In addition, students will have experience using electronic circuit simulation tools and laboratory instrumentation. This preparation is typically acquired through undergraduate coursework in circuits, electronics, signal processing, control systems, and digital design.
28. Mixed-Signal PCB Design for Wearable Bioinstrumentation
This project focuses on the development and prototyping of a custom, low-power mixed-signal Printed Circuit Board (PCB) designed for wearable bioinstrumentation. High-fidelity tracking of general-purpose biosignals is critical for modern digital health applications, but continuous monitoring remains constrained by the energy consumption of conventional signal processing architectures. To address this bottleneck, this project bridges specialized hardware prototyping with brain-inspired computing paradigms.
The primary objective is to design a compact, wearable hardware platform that integrates an Analog Front-End (AFE) for multi-biosignal acquisition (specifically targeting PPG, ECG, or EMG), an Inertial Measurement Unit (IMU), and a central processing unit (incorporating a low-power MCU or FPGA). While the immediate focus of this specific project is the physical hardware layout, component selection, and noise-shielding architectures, the system is explicitly designed as a foundational infrastructure to host energy-efficient neuromorphic algorithms on-device. To achieve this, three distinct interns will be recruited to focus on dedicated hardware implementations: one specializing in ECG front-ends, one in PPG front-ends, and one in integrated EMG/IMU multi-sensor fusion.
The intern will be responsible for schematic capture, multi-layer PCB layout routing, mixed-signal hardware bring-up, and a core C++ or HDL module for capturing signals. By isolating clean biological signals and optimizing the underlying hardware topology, this project directly advances the hardware efficiency and deployment capabilities required for real-time, edge-based intelligent health-monitoring devices.
Research area, student roles & skills
Research area: Our research advances neuromorphic computing and its applications in edge AI and digital health technologies. We develop custom brain-inspired computational models alongside ultra-low-power neuromorphic hardware architectures. By tightly coupling these hardware-software systems, our goal is to enable real-time, on-device biosignal processing directly at the edge.
Student roles: During this 12-week internship, the student will play a hands-on role in the hardware development lifecycle of our wearable bioinstrumentation platform. Working in a collaborative laboratory environment, the intern will transition from initial component selection to physical hardware validation.
Key Responsibilities: - Schematic Capture & Component Selection: Review datasheets to select appropriate Analog Front-Ends (AFEs), microcontrollers/FPGAs, Inertial Measurement Units (IMUs), and power management ICs. Each student will take ownership of a specific sensing pipeline (either ECG, PPG, or EMG/IMU integration) to define the system hierarchy and map signal paths. - Multi-Layer PCB Layout: Utilize industry-standard EDA tools (e.g., KiCad) to design a compact, wearable form-factor PCB. This includes establishing proper grounding strategies, routing differential pairs, and implementing noise-shielding techniques essential for preserving low-voltage biological signals. - Hardware Bring-up & Debugging: Once the fabricated boards arrive, the student will perform power-rail validation, and hardware bring-up. - Laboratory Testing: Use standard diagnostic instruments, including oscilloscopes, digital multimeters, and logic analyzers, to troubleshoot hardware bugs, isolate signal noise, and ensure high-fidelity data capture from the sensors. - C++/HDL coding: Write a firmware in C++ for MCUs or HDL for FPGAs for capture signals. - Documentation: Maintain clear design documentation, including schematics, bills of materials (BOM), and a final summary report detailing the hardware validation results.
Through this role, the student will gain critical experience in mixed-signal hardware design, directly contributing to the foundational physical infrastructure required to deploy energy-efficient neuromorphic algorithms at the edge.
Skills required: Candidates should be upper-year undergraduate students in Electrical/Electronics Engineering or relevant fields. Required Skills: - PCB Design: Proficiency in schematic capture and multi-layer PCB routing (KiCad). - Hardware/Analog: Strong understanding of analog/mixed-signal circuit design and noise-shielding principles. - Components: Familiarity with microcontrollers, sensors, and Analog Front-Ends (AFEs). Specific interest or prior coursework/projects in ECG circuitry, optical PPG sensing, or EMG/IMU integration is highly desirable. - Lab Testing: Experience with hardware bring-up and standard laboratory debugging equipment (oscilloscopes, multimeters). -- Embedded Programming: Experience with C++ programming for microcontrollers or HDL coding for FPGAs.
29. Next-Generation Edge AI Architectures for Intelligent Biomedical Devices
The rapid adoption of artificial intelligence (AI) in healthcare is creating a growing demand for intelligent biomedical systems capable of processing physiological data locally, securely, and in real time. Applications such as wearable health monitors, implantable medical devices, smart biosensors, and portable diagnostic platforms require fast and reliable decision-making while operating under strict constraints related to power consumption, memory capacity, reliability, and patient data privacy. However, conventional AI hardware platforms are often too energy-intensive and computationally demanding for these resource-constrained environments. This project aims to develop next-generation, ultra-low-power architectures for biomedical edge AI by leveraging emerging technologies such as micro-electro-mechanical systems (MEMS), advanced nanoelectronic devices, and hardware-efficient machine learning algorithms. The research will focus on the co-design of AI models, electronic circuits, and system architectures to enable real-time intelligence directly at the sensor level. By integrating low-precision neural networks, near-sensor computing strategies, and energy-aware hardware designs, the proposed solutions will significantly reduce power consumption, memory requirements, and computational complexity while maintaining the accuracy and robustness required for biomedical applications. The project will also investigate intelligent microsystems that combine sensing, signal processing, and decision-making capabilities within compact and autonomous platforms. Particular emphasis will be placed on reliability, security, and long-term operation to ensure safe deployment in clinical, wearable, and implantable healthcare systems. In the long term, this research will establish scalable technologies for next-generation biomedical AI systems supporting continuous health monitoring, early disease detection, personalized medicine, and remote healthcare delivery. The expected outcomes will contribute to improved patient care, enhanced data privacy, and broader accessibility of AI-enabled healthcare technologies.
Research area, student roles & skills
Research area: One of my research areas focuses on the development of smart circuits and systems for biomedical applications. In Dr. Jafari’s lab, we specialize in the design and implementation of smart, real-time systems tailored for on-device data processing. These technologies support personalized healthcare by enabling continuous monitoring of disease progression and precise drug level management. Our work contributes to the advancement of energy-efficient, secure, and user-adaptive tools that facilitate real-time health monitoring and informed decision-making in both clinical and everyday settings.
Student roles: The student will assist in the design, simulation, and development of circuits and systems for smart biomedical sensors. Responsibilities include analyzing signals, implementing circuits and systems, data analysis algorithms, and supporting sensor testing and validation. The student may also contribute to data collection, and performance evaluation. They will work under supervision, participate in lab meetings, and help interpret experimental results. As experience grows, they may assist with troubleshooting or refining the circuits. This role provides hands-on experience in biomedical engineering and digital health technologies while contributing to the lab’s research objectives.
Skills required: The ideal student should have a background in biomedical, electrical, or computer engineering, or a related field. Familiarity with biosensors, physiological signals, basic biomedical signal processing, and programming skills (e.g., MATLAB) is considered an asset. A strong interest in personalized healthcare, along with curiosity, responsibility, and the ability to work both independently and collaboratively, is essential. Prior laboratory experience is not required, as full training will be provided. Most importantly, the student should demonstrate motivation and a strong willingness to learn.
30. Next-Generation Multiplex Biosensors for Early Cancer Diagnosis and Precision Medicine Applications
Cancer remains one of the leading causes of morbidity and mortality worldwide, driven by complex molecular mechanisms that regulate cell growth, proliferation, and survival. Under normal physiological conditions, cell proliferation is tightly controlled by interconnected signaling pathways that ensure proper tissue development and maintenance. However, dysregulation of key pathways, including CDK, Hippo, p53, Ras-MAPK, and retinoblastoma (Rb), can lead to uncontrolled cell division and tumor progression. Understanding the dynamic interactions among these pathways is essential for advancing cancer research and developing more effective targeted therapies. Current methods used to investigate signaling pathway activity often rely on labor-intensive, time-consuming, and costly laboratory techniques. These limitations restrict their ability to provide rapid, comprehensive, and real-time information on the molecular mechanisms driving cancer progression. Innovative biosensing technologies offer a promising alternative by enabling sensitive, accurate, and cost-effective detection of disease-related biomolecules and their biological activity. This project aims to develop a next-generation biosensing platform capable of simultaneously detecting, quantifying, and monitoring the activation states of multiple cancer-related biomarkers involved in key cellular signaling pathways. The short-term objectives include identifying clinically relevant molecular targets, developing appropriate biorecognition strategies, and designing, fabricating, and validating an advanced biosensor with high sensitivity, specificity, and multiplexing capabilities. Particular emphasis will be placed on optimizing sensor performance to enable reliable analysis of samples derived from cell cultures, blood specimens, and tumor tissues. In the longer term, the proposed platform will provide researchers and clinicians with a powerful tool to investigate the molecular networks that drive cancer initiation, progression, and therapeutic resistance. By enabling a deeper understanding of pathway interactions and real-time biomarker monitoring, this technology could support earlier diagnosis, improved treatment selection, and the development of personalized therapeutic strategies. Ultimately, the project aims to contribute to more effective cancer management, reduced treatment-related side effects, and improved patient outcomes.
Research area, student roles & skills
Research area: My research program focuses on the development of next-generation biosensing technologies for the early detection, monitoring, and personalized treatment of chronic diseases, including cancer and neurodegenerative disorders. In Dr. Jafari’s laboratory, we design, fabricate, and validate advanced biosensor platforms capable of detecting and quantifying disease-related biomarkers in biological fluids with high sensitivity and specificity. These technologies enable rapid and accurate diagnosis, prognosis, and treatment monitoring. We also develop biosensors for therapeutic drug monitoring in blood and serum, supporting personalized medicine by optimizing drug dosage, maintaining therapeutic efficacy, and minimizing adverse effects.
Student roles: The student will assist with both simulation and experimental work related to the development of biosensors for the early diagnosis and personalized treatment of chronic diseases such as cancer and Alzheimer’s. Their role will include the analysis, design, and simulation of various biosensors based on different technologies. They will also support the development, fabrication, and characterization of the designed biosensors. The student will receive full training and work under the supervision of experienced team members. They will contribute to data collection, participate in lab meetings to discuss findings, and help interpret the obtained results. As they gain experience, they may also assist with protocol development or troubleshooting. This role is designed to provide the student with hands-on experience in biosensor design and development, while contributing meaningfully to the lab’s overall research objectives.
Skills required: Previous engineering or laboratory experience is not required, as all necessary technical skills will be taught during the internship. The student will learn how to simulate biosensor structures and analyze their performance, as well as gain familiarity with fabrication processes and the functionality of these biosensors. While an interest in biosensors and engineering concepts is beneficial, the most important qualities are curiosity, responsibility, and a strong willingness to learn. The student should also be comfortable working in a team environment and capable of carefully following established protocols.
31. Non linear MEMS and Circuits for Random Number Generation
Supervisor: Frederic Nabki
University: École de Technologie Supérieure (Montréal campus)
Microelectromechanical systems (MEMS) resonators have emerged as powerful platforms for exploiting nonlinear dynamical phenomena at the microscale, offering rich bifurcation behavior, high sensitivity to initial conditions, and low power consumption. Recent advancements in nonlinear circuit design have opened new possibilities for harnessing deterministic chaos and stochastic resonance through the tight coupling of mechanical and electrical degrees of freedom. Among these approaches, the deliberate engineering of geometric and material nonlinearities stands out due to its capacity to generate broadband, high-entropy signals directly from physical noise sources.
This research project focuses on the development and characterization of a novel hybrid system that combines nonlinear MEMS resonators with dedicated analog and mixed-signal circuits for true random number generation (TRNG). The aim is to exploit the synergistic advantages of MEMS intrinsic nonlinearities, parametric amplification regimes, and noise-driven inter-well switching and integration benefits of CMOS-compatible circuitry. This hybrid platform is designed to enhance the performance of hardware random number generators in terms of entropy rate, unpredictability, and resilience against environmental bias.
A central objective of this work is to systematically characterize the nonlinear electromechanical behavior of the hybrid resonator-circuit system across a range of operating conditions. Key parameters such as bifurcation thresholds, noise spectral density, inter-well transition rates, and statistical entropy metrics will be measured and analyzed.
Ultimately, this research aims to push the boundaries of physically grounded randomness generation, establishing nonlinear MEMS as a viable and advantageous entropy source for cryptographic, scientific, and stochastic computing applications.
Research area, student roles & skills
Research area: My research interests include microelectromechanical systems (MEMS) and RF/analog microelectronics, specifically focusing on the creation of next-generation MEMS processes using advanced materials, the integration of MEMS devices with CMOS systems, the modeling of MEMS devices, and the design of CMOS phase-locked loops, ultra-wideband transceivers, and MEMS interface circuits.
My overarching goal is to create innovative integrated systems that achieve unprecedended levels of miniaturization and performance for applications such as sensors or communication systems.
Student roles: Do a brief litterature survey. Characterization of the MEMS Design electronic circuits. Simulate their behavior. If time permits, send a device out to micro-fabrication or a circuit to fabrication. Some laboratory hands-on testing of existing devices may also be done.
Skills required: Some knowledge of eletronics with ideally basic knowledge of classical mechanics, ultrasounds or waves. Knowledge of micro-mechanical modeling using computer aided design tools such as ANSY, Comsol or Coventorware are assets but are not necessary as training can be done during the intership.
32. Non linear MEMS and Circuits for neuromorphic signal processor for edge AI
Supervisor: Frederic Nabki
University: École de Technologie Supérieure (Montréal campus)
Microelectromechanical systems (MEMS) resonators have emerged as powerful platforms for exploiting nonlinear dynamical phenomena at the microscale, offering rich bifurcation behavior, spike-like transient responses, and intrinsic sensitivity to weak input signals. Recent advancements in nonlinear circuit design have opened new possibilities for emulating neural computation through the tight coupling of mechanical and electrical degrees of freedom. Among these approaches, the deliberate engineering of Duffing-type stiffness nonlinearities and parametric instabilities stands out due to its capacity to mimic the threshold-driven, integrate-and-fire dynamics characteristic of biological neurons.
This research project focuses on the development and characterization of a hybrid system combining nonlinear MEMS resonators with analog neuromorphic circuits for edge AI signal processing. The aim is to exploit the synergistic advantages of MEMS intrinsic nonlinear dynamics — including bifurcation-based thresholding, stochastic resonance, and coupled oscillator synchronization — with low-power analog circuit architectures capable of performing inference directly at the sensor level. This hybrid platform targets key neuromorphic functionalities such as spike encoding, pattern recognition, and adaptive filtering without digital overhead.
A central objective of this work is to systematically characterize the electromechanical behavior of the resonator-circuit system across varying drive conditions and coupling topologies. Key parameters such as bifurcation thresholds, spiking rates, inter-oscillator synchronization, and classification accuracy will be measured and analyzed.
Ultimately, this research aims to establish nonlinear MEMS-circuit co-design as a viable hardware primitive for ultra-low-power neuromorphic processors deployable in resource-constrained edge AI environments.
Research area, student roles & skills
Research area: My research interests include microelectromechanical systems (MEMS) and RF/analog microelectronics, specifically focusing on the creation of next-generation MEMS processes using advanced materials, the integration of MEMS devices with CMOS systems, the modeling of MEMS devices, and the design of CMOS phase-locked loops, ultra-wideband transceivers, and MEMS interface circuits.
My overarching goal is to create innovative integrated systems that achieve unprecedended levels of miniaturization and performance for applications such as sensors or communication systems.
Student roles: Do a brief litterature survey. Characterization of the MEMS Design electronic circuits. Simulate their behavior. If time permits, send a device out to micro-fabrication or a circuit to fabrication. Some laboratory hands-on testing of existing devices may also be done.
Skills required: Some knowledge of eletronics with ideally basic knowledge of classical mechanics, ultrasounds or waves. Knowledge of micro-mechanical modeling using computer aided design tools such as ANSY, Comsol or Coventorware are assets but are not necessary as training can be done during the intership.
33. Orchestrating 21 Smart Electrodes: Building a Brain Monitoring Network
Clinical EEG requires 21 electrodes positioned across the scalp, all sampling brain activity simultaneously at 250-1000 samples per second. When each electrode has its own microprocessor, coordinating them becomes a fascinating systems engineering challenge. How do you ensure all 21 channels stay synchronized? How do you detect if one electrode loses contact? How do you efficiently transmit data from 21 sources to a central hub? You'll solve these problems.
Your work focuses on the firmware layer that makes our multi-AIE system function as a cohesive unit. You'll implement a daisy-chain communication architecture where electrodes pass data sequentially to a central module—a design that minimizes wiring but requires careful timing management. You'll develop the master controller firmware that polls each AIE, collects its data, and packages everything into synchronized multi-channel EEG records.
Reliability is critical in medical devices. You'll implement health monitoring routines that continuously assess each electrode's status: Is it reading reasonable voltages? Is it communicating reliably? Are timestamps consistent? You'll create diagnostic protocols that help clinicians quickly identify and troubleshoot issues—because a malfunctioning electrode could mean missing a seizure event.
The testing component is equally important. You'll design comprehensive test suites that validate system performance under various conditions: What happens when an electrode temporarily loses connection? How does the system handle rapid head movements? Can it maintain synchronization over 24-hour recording sessions? You'll use signal generators, oscilloscopes, and custom test fixtures to stress-test the system, documenting performance characteristics and failure modes.
This isn't theoretical work—you'll be testing actual hardware with real custom chips, contributing directly to a medical device that could help epilepsy patients monitor their brain activity at home.
Research area, student roles & skills
Research area: Our laboratory is pioneering distributed intelligence in brain monitoring systems. Instead of using passive electrodes connected to a central recorder, we've developed Active Intelligent Electrodes (AIEs)—each containing its own microchip for signal processing and artifact removal. This summer's challenge is system-level integration: coordinating 21 independent AIEs into a synchronized network that delivers clinical-grade brain monitoring. You'll develop the firmware that orchestrates this electrode network, implementing communication protocols, data synchronization, and real-time quality monitoring. This project sits at the intersection of embedded systems, real-time computing, and medical device development.
Student roles: You'll lead the firmware development and system-level testing for our multi-electrode brain monitoring platform, working at the critical junction where individual components become a functioning medical device.
Firmware Development (50%): Design and implement the central controller firmware that orchestrates 21 AIEs. You'll develop the master polling algorithm that queries each electrode in sequence, collecting data while maintaining strict timing constraints. You'll implement the daisy-chain communication protocol, creating packet structures that efficiently transmit data from multiple electrodes. Power management is crucial—you'll develop sleep/wake routines that maximize battery life while ensuring the system can sustain 24-hour recording sessions. You'll also implement robust error handling: detection of lost packets, electrode disconnections, and communication errors, with appropriate recovery strategies.
System Integration (30%): Work closely with the PCB design team and algorithm developers to ensure firmware interfaces correctly with hardware and provides data in formats required by machine learning pipelines. You'll define communication specifications, implement standardized data formats, and create APIs that other team members can use. You'll participate in integration testing sessions where individual components are combined into the complete system. Testing and Validation (20%): Develop comprehensive test protocols that validate system functionality, reliability, and performance. You'll create automated test scripts, design stress tests that push the system to its limits, and document performance characteristics. You'll use lab equipment to verify timing accuracy, measure power consumption, and validate data integrity. When bugs arise, you'll systematically isolate them—distinguishing firmware issues from hardware problems—and implement fixes.
You'll document your work thoroughly, creating technical specifications and user guides. You'll present at weekly meetings and prepare a final report describing system architecture, testing results, and recommendations for future improvements. This role offers deep experience in embedded systems development, real-time programming, and medical device engineering.
Skills required: Strong embedded C/C++ programming skills Understanding of communication protocols (SPI, I2C, UART) and timing-critical systems Experience with microcontroller development (STM32, Arduino, or similar platforms) Debugging skills using logic analyzers, oscilloscopes, and serial monitors Understanding of real-time systems and interrupt-driven programming Systematic testing and documentation abilities Basic knowledge of signal processing concepts helpful Interest in biomedical applications and medical device development Ability to work independently while collaborating with hardware and algorithm teams
34. Piezoelectric Micro Electro Mechanical ultrasound transducers: Impact of the anchoring
Supervisor: Frederic Nabki
University: École de Technologie Supérieure (Montréal campus)
Ultrasound technology has been in use in many applications ranging from medical diagnosis to non-destructive testing. Ultrasound provides an excellent trade-off between system performance, cost and design complexity.
However, most ultrasound transducers, devices that generate sound waves from electrical signals, are bulky and non-amenable to high degrees of integration precluding complex imaging arrays to be formed or compact use cases. This has limited the use of ultrasound to application where form factors are relatively large and where a limited number of interconnected transducers are needed.
The goal of this research project is to investigate and implement novel micro-machined ultrasound transducers using micro-fabrication techniques and leveraging piezoelectric effects and electrostatic effects. We also wish to create a completely integrated ultrasound imaging system (footprint of a few cm-squared) that will be amenable to mobile electronics and ultra-low-cost applications. This will democratise further the use of ultrasound technology in a wide range of applications and enhance its applicability by enabling very dense ultrasound arrays. This system will require not only transducers but also circuitry (e.g., integrated circuits and printed circuit boards) to drive and process the signals stemming from arrays of these micro-machined transducers.
Research area, student roles & skills
Research area: My research interests include microelectromechanical systems (MEMS) and RF/analog microelectronics, specifically focusing on the creation of next-generation MEMS processes using advanced materials, the integration of MEMS devices with CMOS systems, the modeling of MEMS devices, and the design of CMOS phase-locked loops, ultra-wideband transceivers, and MEMS interface circuits.
My overarching goal is to create innovative integrated systems that achieve unprecedended levels of miniaturization and performance for applications such as sensors or communication systems.
Student roles: Do a brief litterature survey. Characterization of MEMS ultra-sounds transducers Design ultra-sounds transducers or electronic circuits. Simulate their behavior. If time permits, send a device out to micro-fabrication or a circuit to fabrication. Some laboratory hands-on testing of existing devices may also be done.
Skills required: Some knowledge of eletronics with ideally basic knowledge of classical mechanics, ultrasounds or waves. Knowledge of micro-mechanical modeling using computer aided design tools such as ANSY, Comsol or Coventorware are assets but are not necessary as training can be done during the intership.
35. Smart Active Electrode Development: From Chip to Brain Interface
You'll join our team at a critical milestone: transforming custom-designed integrated circuits into functional medical-grade electrodes. Our laboratory has just received fabricated chips from our AIE design—specialized microchips that can record brain signals, monitor electrode quality, and perform real-time signal processing. Your mission is to bring these chips to life through PCB design and embedded system integration.
You'll design multi-layer printed circuit boards that interface our custom ICs with electrodes, power management, and wireless communication systems. This involves schematic capture, PCB layout in tools like Altium or KiCad, and careful consideration of signal integrity for microvolt-level brain signals. You'll work alongside PhD students who designed the chips, gaining rare insight into the full integrated circuit development cycle from silicon to system.
The embedded programming component involves developing firmware for the AIE control systems—implementing communication protocols, power management routines, and data acquisition pipelines. You'll write code that runs directly on our custom hardware, controlling how brain signals are captured and transmitted to the central processing module.
This isn't a simulation project—you'll build and test actual prototypes. You'll use oscilloscopes and signal analyzers to validate your designs, troubleshoot real hardware issues, and iterate based on performance measurements. By summer's end, you'll have created functional brain-monitoring electrodes that could impact epilepsy patients' lives. You'll gain hands-on experience with professional PCB design workflows, low-power embedded systems, biomedical signal acquisition, and the exciting intersection of custom IC design and medical device development.
Research area, student roles & skills
Research area: Our lab develops wearable brain-computer interfaces for real-time neurological monitoring. We design custom integrated circuits (ICs) that process brain signals directly at the electrode, enabling continuous ambulatory EEG monitoring for epilepsy patients. Our Active Intelligent Electrodes (AIEs) combine cutting-edge analog circuit design with embedded machine learning to detect and remove motion artifacts in real-time. This summer, we're integrating our newly fabricated custom chips into compact PCB assemblies that will be worn by patients in their daily lives, transforming how we diagnose and manage neurological disorders.
Student roles: You'll take ownership of the PCB design and embedded system integration for our Active Intelligent Electrodes, working in close collaboration with PhD students developing the custom ICs and graduate students working on the mechanical assembly.
Your responsibilities will include:
PCB Design (40%): Create schematic diagrams and multi-layer PCB layouts for AIE modules. You'll interface our custom chips with power regulators, signal conditioning circuits, and communication interfaces. This includes component selection, footprint creation, routing high-speed signals, and preparing manufacturing files. You'll iterate on designs based on testing feedback and work within tight size constraints for wearable applications.
Embedded Programming (40%): Develop firmware for the AIE control systems using embedded C/C++. You'll implement SPI/I2C communication protocols to control the custom chips, create data acquisition routines that capture brain signals at precise sampling rates, and develop power management algorithms to maximize battery life. You'll also implement basic diagnostic routines for testing and troubleshooting.
Testing and Validation (20%): Build and test prototype boards, using lab equipment to verify electrical performance. You'll measure signal quality, power consumption, and communication reliability. When issues arise, you'll debug both hardware and firmware, learning to distinguish between PCB layout problems and software bugs. Throughout the project, you'll attend weekly team meetings, present your progress, and receive mentorship from both the principal investigator and senior graduate students. You'll gain exposure to the complete IC design workflow—a rare opportunity to see how chips are designed, fabricated, and integrated into real-world systems. This experience bridges electrical engineering, embedded systems, and biomedical applications, providing skills highly valued in both academia and industry.
Skills required: - PCB design experience (academic projects acceptable; familiarity with Altium, KiCad, or Eagle) - Embedded C/C++ programming for microcontrollers (Arduino, STM32, or similar platforms) - Basic understanding of analog and digital circuits - Experience with oscilloscopes and multimeters for hardware debugging - Strong problem-solving skills and attention to detail - Enthusiasm for hands-on hardware development - Interest in biomedical applications is a plus but not required - Ability to work collaboratively in an interdisciplinary research environment
36. Ultra-wideband Wireless Integrated Circuits for Ultra-low Power Communications
Supervisor: Frederic Nabki
University: École de Technologie Supérieure (Montréal campus)
Location: Montreal, Québec
Start date: 2027-05-03 (flexible)
Disciplines: Electronic Systems, Engg-Electrical, Engg-Electrical, Engineering, Studies Science and Technology
This research focuses on the design of an ultra wideband (UWB) transceiver for use in an innovative wireless communication system for wireless sensors including active RFID tags, environmental sensors, positioning sensors, wireless headsets or smart phones. Given the small amount of energy available in the batteries of these devices and the importance of their autonomy, it is essential to minimize the energy consumption of wireless transceivers needed in these applications. In order to attain this goal, ultra-wideband technology has the potential to significantly reduce the energy consumption of transceivers in comparison to what is possible with traditional architectures. That said, the design of an ultra wideband transceiver that allows continuous operation while dissipating energy ten times lower than the state of the art is a challenge that requires a sound architecture. The ultimate objective is to get energy consumption so low that it will be possible to operate wireless devices much longer with small batteries or even just use energy harvesting devices such as photovoltaic cells to operate the wireless device without the need for batteries.
The project goal is to develop innovative integrated circuits for use in a UWB transceiver architecture and design printed circuit boards for the testing of existing innovative circuits introduced by our group.
Research area, student roles & skills
Research area: My research interests are mixed-signal and radiofrequency integrated circuits and microelectromechanical systems (MEMS) for various applications including sensing and communications. Some research projects include the design of novel low-power and high speed analog-to-digital converters, CMOS phase-locked loops, ultra-wideband transceivers, and wired communication channels. In addition, I am active in research projects involving the creation of next generation MEMS fabrication processes using advanced materials, the integration of MEMS devices with CMOS systems, and the modeling of MEMS devices.
Student roles: Depending on his background, the intern will be involved with the design (in CMOS technology) of the signal generator, mixer, the low-noise amplifier or the filters used in the overall system. Design will be done by using commercial computer aided design tools such as Cadence, SpectreRF and Hspice). The intern will be supported by graduate students and a postdoctoral fellow. The intern can also be involved in system level laboratory testing, modelling and simulation.
Skills required: The students should have taken basic courses in the field of analog electronics design. Some knowledge of standard wireless transceiver architectures (e.g., heterodyne, homodyne) is an asset. Knowledge of radiofrequency electronics is an asset. Knowledge of integrated circuits design is an asset.
We are interested in developing custom-built equipment for monitoring animal behaviour and brain activity. The ideal candidate will have a strong background in mechatronics, microcontrollers, and Internet of Things (IoT) systems. They should be proficient in designing mechanical parts and discrete electronic PCBs, experienced in fabricating components with 3D printers and other tools, and adept at developing and debugging hardware-software interfaces. Experience in neuroscience research is a bonus but not required.
Research area, student roles & skills
Research area: The Neurotechnology Group at the Douglas Research Center, McGill University, led by Professor Majid Mohajerani, has been involved in several neurotechnology and Neuroengineering projects. Using different methodologies, the team has developed an automated system for monitoring brain activity and behaviours. With the development of microcomputers and sensors, it is possible to automate the collection and analysis of data with minimal human intervention. The results of our work have been published in high-impact-factor journals and are now used in many research laboratories worldwide.
Student roles: The student will join a small team and assist with the mechanical and electrical design, manufacture, and testing of instrumentation for use in several research labs across North America and Europe. The candidate will work closely with the software teams to develop new products and improve existing ones. Building on the success of previous and existing systems, a new class of intelligent systems will replace current methods for assessing brain activity.
Skills required: Key Responsibilities:
* Design and develop mechanical components using computer-aided design (CAD) software. * Fabricate prototypes and parts utilizing 3D printing technology and other fabrication tools. * Design and develop discrete printed circuit boards (PCBS) for various applications. * Integrate and troubleshoot IoT devices and systems. * Develop and debug code for hardware-software interfaces in IoT applications. * Collaborate with cross-functional teams to support ongoing research projects. * Maintain detailed documentation of designs, processes, and test results.