Engg-Biomedical

Research area: My research focuses on interfacial science and how materials behave where two surfaces meet, such as between liquids and solids. I study how these interactions change in reduced gravity environments, like space or the Moon. In low gravity, forces like surface tension become more important than weight, which affects how liquids spread, stick, or form shapes. Understanding this helps improve technologies like 3D printing, coating, and fluid handling in space. My work aims to make these processes more reliable and efficient, supporting future space missions while also offering insights that can improve material design and manufacturing here on Earth.

Student roles:
-image processing
-CAD modelling
-Experimental design and assembly
-Data analysis

Skills required:
The ideal student should have a background in mechanical engineering, materials science, physics, or a related field. Basic knowledge of engineering and mathematics is required. The student should be comfortable with problem-solving, data analysis, and using software tools for modeling or design. Strong communication skills and the ability to work both independently and in a team are important. An interest in space research, innovation, and hands-on experimentation will help the student succeed in this project.

Research area: My research program is focused on impact biomechanics, aiming to enhance the safety and survivability of injurious events, in motor vehicle and defence-related applications. This field of study seeks to understand the human body response to high-rate loading, the mechanism for injury or trauma, and methods to mitigate this injury. This involves investigating the effect of applied loading on post-mortem human subjects, or ‘PMHS’, and the effective translation to surrogates; either mechanical, such as Anthropomorphic Test Devices, ‘ATDs’ (‘crash test dummies’), or computational, such as finite element model human body models, HBMs.

Student roles:
Under the supervision of the host professor, the student will take primary responsibility for the execution and analysis of the computational simulation study. The student's role encompasses four main phases of work across the 12-week internship.
In the first phase, the student will familiarize themselves with the GHBMC M50 and F50 model architectures, with particular focus on the below-knee skeletal and soft tissue components. They will conduct a structured review of anatomical and geometric differences between the two models relevant to axial load transmission, documenting key morphological parameters including bone cross-sectional geometry, joint alignment, and tissue mass distribution.
In the second phase, the student will develop and verify the simulation setup in LS-DYNA, including isolation of the below-knee segment, application of boundary conditions replicating a 5 m/s plantar axial impact of approximately 150 ms duration, and definition of output requests for force, stress, and strain across target anatomical structures. Both models will be prepared using consistent methodology to ensure comparability of outputs.
In the third phase, the student will execute simulations, perform quality control checks on model stability and energy balance, and extract time-history response data. They will systematically compare load propagation pathways between the M50 and F50, identifying where and when load distribution diverges between models and characterizing the structural features driving observed differences.
In the final phase, the student will synthesize findings into a technical report suitable for journal submission and prepare a formal presentation of results. The student will work closely with the supervising professor throughout, participating in regular progress meetings and receiving guidance on scientific interpretation, academic writing, and research methodology in injury biomechanics.

Skills required:
The student should have an undergraduate or graduate background in mechanical engineering, biomedical engineering, or a closely related field, with demonstrated coursework or experience in finite element analysis and solid mechanics. Familiarity with LS-DYNA or equivalent explicit FE software is strongly preferred. Experience with computational biomechanics, musculoskeletal modelling, or human body model simulation is an asset. The student should be comfortable working with large-scale FE models, interpreting force-displacement and stress/strain outputs, and communicating technical findings in written and oral form. Programming experience in MATLAB or Python for post-processing is beneficial but not required.

Research area: Dr. Albert Vette is currently a tenured Full Professor in the Department of Mechanical Engineering, University of Alberta, and a Research Scientist at the Glenrose Rehabilitation Hospital, Edmonton, Canada. His work is centered at the interface between musculoskeletal biomechanics, neuromuscular control, and rehabilitation engineering. Particular research interests of Dr. Vette include the dynamics and control of neuromuscular processes; the modeling of physiological systems; sensory-motor integration during human movement and posture; and assistive technology for individuals with neurological disorders.

Student roles:
Guided by the supervisor and a senior graduate student, the undergraduate student will be responsible for:
- exploring different hardware options and EMG processing algorithms for FES triggering (as part of Aim 1);
- prototyping a test-bed for evaluating different EMG processing algorithms and communication protocols (as part of Aim 2); and
- analyzing the experimental data obtained in Aim 3.
As such, the undergraduate student will be exposed to a range of experimental and analytical techniques.

Skills required:
The student should have a background in Mechanical, Computer, Electrical, or Biomedical Engineering - or any related field. Students should have some knowledge in data acquisition, processing and analysis, and be generally interested in the biomechanics research field.

Research area: At IDEA Lab (see our website at: goidealab.com) at the University of Alberta, we are focused on developing autonomous intelligent systems to deliver personalized health, specially using wearable, artificial intelligence, and robotic systems. Using these technologies, we measure movement and physiological data. With cost-effective robotic systems, we can perform assessment of human function and deliver therapy. Commonly, we combine these with artificial intelligence (e.g., deep learning) and biomedical signal processing.

Student roles:
The student's role in this project includes:
Data Collection: Integrating physiological sensors to collect GSR and HR data from users during VR gameplay. Preprocessing this data to ensure its suitability for analysis.
Data Analysis: Developing algorithms to interpret the physiological data, quantifying cognitive load and engagement. This includes statistical analysis to identify trends and correlations between gameplay content and user responses.
Content Adaptation: Creating mechanisms to dynamically modify VR content based on the user's physiological responses. This involves designing strategies to balance cognitive load and maintain engagement, particularly for therapy games.
Testing: Evaluating the system's performance in various VR scenarios, assessing its ability to enhance user experiences and therapy outcomes.
Reporting: Documenting the project's progress, methodologies, and findings. Preparing technical reports and contributing to potential academic publications, offering insights into the integration of physiological feedback into VR gaming and therapy.

Skills required:
The ideal student for this project should possess:
A background in computer science, biomedical engineering, or a related field, with coursework in physiology and data analysis.
Proficiency in programming languages such as Python and C#/C++ for VR environment development.
Understanding of physiological sensors, including GSR and HR monitoring devices, and how to interpret their data is an ASSET.
Familiarity with VR systems and gaming design principles is an ASSET.

Research area: Prof. Charles-Francois de Lannoy’s research is transforming the paradigm of purification technologies and environmental remediation using advanced membranes, electrochemical technologies, nanomaterial composites, and nanosorbents. He is one of the first inventors of electrically conductive membranes and he and his research group are pioneers in using electrochemical technologies for separations and purification applications. These applications includes anti-fouling membranes that can be used in bioreactors, wastewater treatment, biopharmaceutical purifications, and many other industrial processes that contain bacterial colonies.

Student roles:
Summer research students will be engaged in:
1) synthesizing suspensions of nanomaterials in aqueous solutions and solvents
2) synthesizing electrically conductive membranes using film casting techniques
3) setting up electrochemical experiments
4) analyzing the surface of membranes based on various foulants

Students will receive training from a PhD student nanomaterial fabrication, membrane development, and electrochemical techniques.

Students will develop presentation skills through group meeting presentations, poster presentations at the end of the term, and a technology pitch competition internal to the department.

Skills required:
Required skills:
- experience working in a wet lab, e.g. handling solvents, pipetting, making solutions and suspensions
- interest in electrochemistry
- a love of material science
- good collaboration
- clear communication

Additional skills that are not necessary:
- experience working with membranes
- experience with proteins and bacteria
- experience with electrochemistry

Research area: Our research focuses on the neuronal substrates involved in the control of human locomotion. Areas of expertise include neuroscience, motor control, computer science and engineering. We use techniques such as movement kinematics, EMG, neuronavigated brain stimulation (TMS) and brain imaging with MRI and PET. We study human locomotion both overground and on a split-belt treadmill.

Student roles:
Analyze brain imaging data and transcranial magnetic stimulation responses.
Collaborate with graduate students to conduct the project.

Skills required:
Programming skills an asset. Interest in human motor control and neuroscience. Interest in teamwork.

Research area: Our team specializes in image recognition, image classification and machine learning in medical imaging. We work closely with clinical collaborators in the medical imaging department of Sainte-Justine Hospital, one of the largest pediatric center in Canada.

Student roles:
This projet will be organized as follows :
1. Meeting with the clinical team and familiarization with the project (1 week).
2. Software design involving : 1) requirements analysis, design and implementation of a software prototype (6 weeks)
3. Validation and technical writing (2 weeks).

Skills required:
The candidate should have interest in software development, 3D and augmented reality for medical imaging research. The selected candidate will be exposed to cutting edge research in interventional imaging.

Research area: Our laboratory specializes in the microfluidic study of blood and complex biological fluids at small scales.We want to explore additional techniques such as droplet generation and acoustofluidics, where acoustic waves are used to manipulate particles and cells within microchannels. The undergraduate intern will contribute to the design and 3D printing of microfluidic components, as well as the integration of Arduino/ESP-based control systems to automate and characterize these platforms. This work sits at the intersection of mechanical fabrication, embedded systems, and experimental fluid physics.

Student roles:
You will:

Explore open‑source scientific hardware and identify opportunities to improve or simplify existing tools
Design mechanical parts using CAD software and fabricate them with 3D printing
Build and program systems using Arduino / ESP‑type microcontrollers, motors, sensors, and actuators
Experiment with motion, vibration, acoustics, optics, or microfluidics
Test your prototypes in realistic lab settings and improve them based on results
Document your work so others can reuse, reproduce, and improve it

This project values curiosity, experimentation, and creativity as much as technical precision.

Skills required:
This project is ideal for undergraduate students who:

Love building, tinkering, or hacking things
Are curious about how lab instruments actually work
Study biomedical engineering, engineering, physics, computer science, or a related field
Enjoy learning by doing (Arduino experience is helpful but not required)
Care about accessible science, global research equity, and open tools

Research area: My lab develops EEG-based brain-computer interface and neurotechnology methods that combine brain rhythms, eye tracking, behavioral performance, and machine learning. We are especially interested in individual differences: why some people produce clean, decodable EEG signals while others are harder to classify.

Student roles:
Two interns will work across data collection, feature engineering, and decoding. Intern 1 will help run EEG/eye-tracking experiments, maintain participant/session metadata, score behavioral performance, and extract core ERP, alpha, gamma, and ocular features. Intern 2 will develop the decoding pipeline: train within-subject classifiers, compare cross-validation strategies, quantify subject-level decoding accuracy, and model which neural/behavioral features predict success. Both interns will contribute to quality control, literature review, and reproducible code. Final deliverables should include cleaned pilot data, a subject-level results table, decoding-performance plots, and a short report on candidate predictors of strong versus weak BCI performance.

Skills required:
Students should have Python experience and interest in EEG, brain-computer interfaces, machine learning, statistics, or cognitive neuroscience. Helpful skills include NumPy/pandas, MNE-Python, scikit-learn/PyTorch, signal processing, data visualization, and careful experimental work. Students should be comfortable debugging noisy real-world data.

Research area: Dr. Albert Vette is currently a tenured Full Professor in the Department of Mechanical Engineering, University of Alberta, and a Research Scientist at the Glenrose Rehabilitation Hospital, Edmonton, Canada. His work is centered at the interface between musculoskeletal biomechanics, neuromuscular control, and rehabilitation engineering. Particular research interests of Dr. Vette include the dynamics and control of neuromuscular processes; the modeling of physiological systems; sensory-motor integration during human movement and posture; and assistive technology for individuals with neurological disorders.

Student roles:
The first task will be to ensure that motion capture markers are labelled properly in both the non-disabled and prosthesis user data sets. Then, the data will be used to calculate three-dimensional joint kinematics for the left and right shoulder using the GaMA software. The trainee will extract and statistically compare relevant kinematic measures, namely range of motion, peak angle, and peak angular velocity. In parallel to this work, the trainee will conduct a literature review and draft a manuscript. The trainee will gain valuable scientific experience within a biomedical research program and apply her/his academic knowledge to a clinically relevant problem. She/he will benefit from a wide range of expertise, learn about clinical challenges and needs as they relate to prosthesis user movement assessment, and develop a professional network that will be beneficial in the future. We plan to publish the work in a clinically relevant journal, which will allow the trainee to gain valuable writing experience and further strengthen her/his resume.

Skills required:
The student should have a background in Mechanical, Computer, Electrical, or Biomedical Engineering - or any related field. Students should have some knowledge in data acquisition, processing and analysis, and be generally interested in the biomechanics research field.

Research area: My research program is dedicated to translating insights derived from non-invasive neuroimaging modalities, specifically electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI), into clinically relevant applications for brain disorders. Leveraging my dual affiliation as a scientist at Simon Fraser University and within local public hospitals, I explore how variability and pathological alterations in these brain signals manifest across diverse neurological populations and throughout typical development and ageing processes. A central aim is to elucidate the functional correlates of this neurophysiological variability, with the ultimate goal of developing and validating clinically translatable computational methods that enhance diagnostic accuracy.

Student roles:
The student intern's primary responsibility will be to analyze associations between patients’ age and electroencephalograms (EEGs) from an extensive clinical database. This will involve employing advanced statistical techniques (largely in Python), including predictive analytics and machine learning models, to identify correlations and potential predictive relationships. Key tasks include data preprocessing, feature extraction, model development, and validation of analytical pipelines.
A significant component of this role involves disseminating research outcomes. The intern will be expected to contribute substantially to, and potentially lead, the preparation of a research manuscript suitable for peer-reviewed publication. This will entail articulating the study's rationale, methodology, results, and their broader scientific implications.
Furthermore, the intern will actively collaborate with other students and researchers within the broader research initiative focused on clinical EEG and data science applications. This collaborative engagement is crucial for exchanging knowledge, addressing methodological challenges, and advancing the collective development of AI tools in clinical neurophysiology and healthcare.

Skills required:
The ideal candidate for this internship will possess a strong academic background in a quantitative discipline. This typically includes fields such as Mathematics, Statistics, Computer Science, Physics, Engineering, or a closely related area with a significant analytical component, providing the foundational knowledge necessary for complex data analysis and modeling.
Familiarity with Python is beneficial, as it will be the primary tool for data manipulation, analysis, and algorithm implementation within this project.
Furthermore, the candidate should have an understanding of statistical principles and techniques. Prior experience applying machine learning concepts, even in academic coursework or personal projects, would be advantageous.

Research area: My laboratory is specialized in: neuroscience, behaviour, immunohistochemistry, rat, brain, reward system, surgeries and electrophysiology. For this project: C: arduino, Python.

Student roles:
To code a graphic interface with menu for 2 or more different behaviour analysis test. Redesing 3D pieces to allow better functioning, less exceptions and bugs.
Make the database more easily accesible.

Skills required:
Bases in programing (Python, Arduino, Raspberry Pi or others), in 3D design and print. PCB desing. Bases in electronics or basic robotics. Interest in Node-Red, SQL.

Research area: I have worked with biological tissues like articular cartilage and skeletal muscles and polymeric hydrogels such as agarose, and gelatin. I answer research questions through carefully designed in vitro, in situ and in vivo experiments and theoretical modelling. I am interested in unravelling the structure- composition-function relationships and cell-tissue interactions in soft connective tissues. By establishing foundational understanding of how mechanical forces are transduced to the cells, I hope to apply this knowledge in the field of tissue engineering with the ultimate goal of bio-fabricating a viable and functional tissue substitute for patients who suffer from soft tissue injuries/diseases.

Student roles:
The student will be required to design the architecture of two separate MEW scaffolds for mechanical reinforcement and for micro-channel formation. The student will then combine the two designs for a multi-material scaffold that can be used to enhance the mechanical and nutrient transport properties of a hydrogel system (e.g., agarose, alginate, gelatin methacryloyl). The student will be trained to operate the MEW printer and perform 3D printing to fabricate the designed scaffold. The student will also be responsible for collecting experimental data (e.g., mechanical testing) to validate the designed scaffold in the lab.

Skills required:
We are looking for highly motivated biomedical engineering students who are strong in engineering design and eager to learn state-of-the-art 3D printing techniques and prototype development. The students should be comfortable with using standard engineering design software such as Solidworks and Fusion 360.

Research area: Research at the Human-Robot Interaction lab of the University of Calgary aims to make human-robot interactions safe, comfortable, and intuitive. We specifically research physical interactions between humans and robots. For instance, by regulating the forces between robots and their environment, we can ensure these powerful machines interact respectfully and reliably with people. We are currently working on increasing a robot's awareness of physical contacts: how can a robot know where it is being touched and with how much force? How can it respond appropriately?

Student roles:
Your task will be to design and conduct experiments using different sensors to detect touch, and to analyze the signals captured by the sensors to characterize how well contacts can be identified using the different sensors. For example, your analysis might include converting signals from the time domain to the frequency domain to distinguish contacts from ambient noise, using triangulation to locate the origin of a sound, or using machine learning to classify contacts.

Skills required:
Prior knowledge of linear algebra, partial derivative equations, programming (Python and/or C++), instrumentation, measurements, signal processing and system analysis will be of great help to complete this project!

Research area: My research focuses on the development of biomaterials based on the principles of tissue engineering, drug delivery, and cell biology, aiming at the regeneration or repair of craniofacial, oral, and dental tissues.

Student roles:
The student will be responsible for completing all necessary training required to carry out the lab-based research project. Under the supervision of a senior lab member and the host supervisor, the student will conduct the proposed project, maintain weekly progress reports, and fulfill all mandatory activities associated with the Mitacs program. At the end of the internship, the student will be expected to submit a final report and contribute to a manuscript based on the work conducted during their internship.

Skills required:
Preference will be given to students with experience in cell culture research, 3D bioprinting technology, and scientific writing, including manuscripts and research reports.

Research area: I study Brain systems, which can be studied at different spatial and temporal scales. For the spatial scale, our research is focused on the circuit – regional level scale. For the temporal scale, we focus at the second to minute level scale. We use datasets obtained from EEG (Electroencephalograms) signals of brain computer interface devices. The datasets are analyzed with several computer science techniques (e.g. deep learning) to achieve models that express the brain mechanisms and help us to get insights of their systems for a variety of neurological conditions. See further details at https://www.acs.uwinnipeg.ca/scamorlinga/research/index.htm

Student roles:
Programming, testing and evaluating modules in our solution tool. Some documentation may be required out of the assigned R&D activities.

Skills required:
Python programming, Generative AI tools to produce code, data analysis, data visualization, testing software at application and system level, nice to have some knowledge of hardware and signal processing.

Research area: We develop advanced optical imaging systems for non-invasive measurement of biological tissue structure and function. Our research focuses on time-resolved diffuse optical techniques, including time-domain time-of-flight (DTOF) sensing, and computational reconstruction methods to convert photon transport measurements into spatial maps of tissue depth and optical properties.

Student roles:
The student will contribute to the development of a time-domain optical scanning system and associated computational reconstruction algorithms. Responsibilities include processing DTOF signals, developing algorithms for depth reconstruction, and validating performance using experimental and simulated datasets.

The student will assist in system calibration, data acquisition, and analysis of photon time-of-flight distributions. They will implement numerical methods to convert temporal photon profiles into spatial depth maps and evaluate reconstruction accuracy under different optical conditions.

The student will work closely with graduate students and postdoctoral researchers in the laboratory and participate in regular research meetings. They will also contribute to visualization of results and preparation of figures for dissemination.

Depending on progress, the student may also assist with experimental system integration, including the alignment of optical components and the optimization of detection parameters.

This project provides interdisciplinary training in optical engineering, computational imaging, and biomedical signal processing.

Skills required:
Background in engineering, physics, computer science, or biomedical engineering. Strong programming skills (Python or MATLAB) required. Familiarity with signal processing, optics, or imaging systems is an asset. Experience with data analysis and mathematical modeling is highly desirable. Interest in optical instrumentation and computational imaging is essential.

Research area: Our research group specializes in the design, fabrication and testing of specialty optical fibers and photonic devices for applications in optical communications and optical sensing. The domains of research and applications also extend to quantum technologies and biomedical sensors.

Student roles:
The intern will learn how to use the Glass fusion processing setup and perform a number of experiments (described above). The intern will report to the supervisor by means of in-person meetings (1/week), written weekly reports and technical reports.

Skills required:
- Experience and proficiency with laboratory instruments (e.g. oscilloscope, etc.)
- Basic knowledge of fiber optics
- Good communication skills
- Ability to work inside a team

Research area: I have worked with biological tissues such as articular cartilage and skeletal muscles and polymeric hydrogels such as agarose, and gelatin. I answer my research questions through carefully designed in vitro, in situ and in vivo experiments and theoretical modelling. I am interested in understanding the structure- composition-function relationships and cell-tissue interactions in soft connective tissues. By understanding how mechanical forces are transduced through multi-scales to the cells, I hope to apply this knowledge in the field of tissue engineering with the ultimate goal of bio-fabricating a viable and functional tissue substitute for patients who suffer from soft tissue injuries/diseases.

Student roles:
The student is required to go through the literature of some of the most popular deep-learning image segmentation tools, and will be tasked to set up the Mask R-CNN in the university servers. The students will go through 3D image stacks collected by state-of-the-art multi-photon microscopy, and apply Mask R-CNN to segment 3D cells with normal and abnormal shapes from the cartilage tissues. Morphological analysis of the segmented cells will be performed. Then, a correlation analysis will be perform to investigate the relationship between the cell morphology and the local tissue stiffness.

Skills required:
We are looking for highly motivated student who is strong in machine learning, and biomedical engineering.

Research area: The primary focus of my research group is to develop, evaluate, and apply new image processing methods, algorithms, and software tools for the analysis of medical images. This includes the image-based extraction of clinically relevant parameters and biomarkers describing the morphology and function of organs. In doing so, we aim to support clinical studies and preclinical research as well as developing and improving computer-aided diagnosis and patient-specific, precision-medicine, prediction models. Additionally, a secondary focus of my research lab is in the area of machine learning, mostly in combination with, but not limited to, medical imaging.

Student roles:
The student will be mainly responsible to develop the advanced convolutional neural networks and image processing methods required for this project. The student will be mentored directly by a senior lab member. Additionally, the student will also be able to take a lead on manuscript writing and preparation for presentation of the results of this project at national and international meetings and scientific journals.

Skills required:
Programming experience (e.g. Java, C++, Matlab, Python) is an essential requisite for this position. Experience or basic understanding of deep neural networks and image or signal processing are helpful but not essential.

Research area: My specialized research area lies at the intersection of Artificial Intelligence, Machine Learning, and hyperspectral image analysis, with a growing focus on explainable AI and hybrid quantum–classical learning frameworks. The work centers on developing advanced spectral–spatial models, including convolutional, transformer-based, and graph-driven architectures, to process high-dimensional data for applications such as precision agriculture, medical imaging, and document forensics. A key aspect of this research is designing interpretable and computationally efficient methods that can operate under limited labeled data while maintaining strong generalization.

Student roles:
The student will work as part of the research team on developing AI models for early disease diagnosis using hyperspectral medical imaging data. Their role will include reviewing related research literature, preparing and preprocessing medical imaging datasets, and implementing deep learning models using Python-based tools. The student will also conduct experiments, analyze model performance, and assist in improving the accuracy and interpretability of the proposed methods. In addition, they will participate in regular research meetings, document their progress, and contribute to preparing technical reports or research publications.

Skills required:
The ideal student should have a background in Computer Science, Biomedical Engineering, Data Science, or a related field, with basic knowledge of machine learning and deep learning techniques. Experience with Python programming and familiarity with frameworks such as PyTorch or TensorFlow will be helpful. Some understanding of image processing, medical imaging, or data analysis would be an advantage, although prior experience in hyperspectral imaging is not required. The student should be motivated to learn new concepts, interested in AI applications in healthcare, and comfortable working both independently and within a collaborative research environment.

Research area: My background is in the area of human factors and biomedical engineering. My specific research interests include neuromuscular physiology and rehabilitation of clinical populations as well as occupational physiology. The focus of my research is the use of surface electromyography (sEMG) to monitor muscle activity during movement. sEMG is the measurement of the electrical activity at the skin’s surface, that brings about muscle contraction. The sEMG can be used to examine muscle activation patterns for a variety of applications including improved prosthesis design (artificial limbs). Understanding neuromuscular function in movement efficiency will help develop better models of human movement.

Student roles:
The student will be responsible to work with the research team to investigate various types of sensor grids with the goal of contributing to the development of a novel sensor. This will require extensive research in existing technologies, working with the research team to develop a prototype and testing to determine the validity of such a system. The student will also be involved in completing a research protocol to test the prototype against a gold standard system. This will require the student be involved with experimental setup, participant recruitment, data collection, data analysis and synthesis.

Skills required:
The student should have a background in biomedical engineering or electrical engineering with a strong interest in human movement and biological sensor technology. The student must have both hardware (electronics) and software (programming in Matllab and/or Python) in order to develop the technology. The skills required also include familiarization with data acquisition equipment and hardware, strong mathematical and statistical analysis skills and the ability to critically analyze published research. The student should also have strong communication skills.

Research area: My laboratory specializes in cardiac imaging. We develop tools for analysis, automation, and interpretation of cardiac imaging datasets. Our main research theme is 4D flow MRI and our secondary themes are related to cardiac imaging strategies.

Student roles:
The student will focus on standard heart fat annotation ~100 bicuspid aortic valve cases

Skills required:
Data analysis, Matlab, Python are assets. Biomedical or Engineering backgrounds.

Research area: The NeuroCognitive Imaging Lab (NCIL) conducts basic and applied cognitive neuroscience research. Much of our research is focused on language and neuroplasticity — how the brain changes with experience. The ultimate goal of our work is to help people live healthier, happier, and more productive lives. Our research primarily uses EEG neuroimaging, combined with data analysis pipelines written largely in Python that include conventional signal processing, machine learning, and AI. Current projects in the lab are focused on brain computer interface development, second language acquisition, reading development in children, and hyperscanning studies of pairs of individuals engaged in conversation.

Student roles:
- Become familiar with necessary background literature for the project
- Become familiar with lab protocols for EEG and eye tracking data collection
- Iteratively implement and test approaches to EEG-eye tracking integration
- Validate EEG data from integrated approach against EEG data from conventional (one word at a time) approach
- Build out software in Python to use the integrated system in a study of reading in children, according to provided specifications
- Validate and test the software from the previous step
- At the end of internship, prepare a written summary of the work and report a summary to the lab group in an oral presentation

Skills required:
Essential skills:
- Python programming
- signal processing
Desired skills:
- experience with EEG data collection
- experience with eye tracking data collection
- experience with EEG data processing
- experience with eye tracking data analysis
- familiarity with the Lab Streaming Layer protocol

Research area: My research program is focused on impact biomechanics, aiming to enhance the safety and survivability of injurious events, in motor vehicle and defence-related applications. This field of study seeks to understand the human body response to high-rate loading, the mechanism for injury or trauma, and methods to mitigate this injury. This involves investigating the effect of applied loading on post-mortem human subjects, or ‘PMHS’, and the effective translation to surrogates; either mechanical, such as Anthropomorphic Test Devices, ‘ATDs’ (‘crash test dummies’), or computational, such as finite element model human body models, HBMs.

Student roles:
The student will conduct experimental biomechanics research evaluating the biofidelity of the THOR lower extremity ATD under axial impact loading. Specifically, the student will investigate how ankle posture influences axial load and bending moment transmission through the lower extremity, using a pneumatic impacting apparatus to deliver applied load. A projectile impacts the plantar surface of the foot, by travelling down an acceleration tube towards the impact chamber via compressed air. Ankle posture will be controlled via an ankle positioner, and the ATD lower extremity suspended overhead within the impact chamber to allow for post-impact motion.
The student will be responsible for designing and executing a repeated-measures impact testing protocol across a range of controlled ankle postures. This includes configuring the THOR lower extremity instrumentation, setting and verifying ankle posture between trials, operating data acquisition systems, and applying appropriate signal processing (filtering, calibration verification) to raw sensor output.
Beyond data collection, the student will analyze results to characterize how posture-dependent changes in load transmission compare across conditions, and will situate their findings within the existing PMHS literature on ankle posture and injury outcome. The ultimate goal is to assess whether the THOR lower extremity captures posture-dependent injury risk in a manner consistent with human cadaveric response data, which is a critical step in validating its use in regulatory impact assessments.
The student will be expected to maintain detailed test documentation, contribute to internal reports, and support preparation of findings for peer-reviewed publication. They will work closely with the supervising faculty member and collaborate with lab personnel and external partners as needed. This is a hands-on, technically demanding role suited to a student with strong laboratory instincts, an interest in injury biomechanics, and a motivation to contribute to research with direct implications for occupant safety in frontal motor vehicle collisions.

Skills required:
Applicants should have (or be pursuing) a degree in mechanical or biomedical engineering, or kinesiology with a biomechanics focus. Preferred skills include experimental research experience (impact or dynamic testing would be a strong asset), instrumentation and data acquisition, and signal processing. Familiarity with ATD operation, lower extremity anatomy, and injury biomechanics is an asset. Strong attention to detail and documentation practices are essential for repeatable test protocols. Experience with MATLAB, Python, or Excel for data analysis is expected. Prior lab-based research experience (e.g., co-op, undergraduate thesis, or equivalent) is also preferred.

Research area: Our lab focuses on understanding immune responses in cancer and discovering biomarkers for early disease detection. Our interdisciplinary research integrates in vitro diagnostics, spectroscopy, and machine learning to investigate the metabolic reprogramming of immune cells within the tumor microenvironment, with applications spanning both cancer and oral health. The goal is to bridge the gap between fundamental scientific discoveries and clinical applications, particularly in the area of early disease detection.

Student roles:
The student will be responsible for developing a customized computational model to link high-dimensional Raman spectral data with transcriptomic profiles. This will involve refining data processing pipelines to clean, normalize, and organize complex spectral and transcriptomic datasets. The student will implement and optimize machine learning models capable of capturing both linear and nonlinear relationships between cellular Raman spectra and gene expression profiles, enabling accurate prediction and interpretation of omics information. The student will design novel feature extraction and representation learning approaches that go beyond simplistic one-to-one mappings between Raman wavenumbers and genes, aiming to uncover meaningful latent features that better represent underlying biological information. An important aspect of the work will be to interpret model outputs in the context of existing biological knowledge, linking predictive features to known molecular pathways, regulatory networks, or cellular states, thereby providing mechanistic insights into the cellular processes that are reflected in the Raman spectra.

Skills required:
The project requires a student with a strong foundation in machine learning, linear algebra, and multivariate statistics, including experience with regression, PCA, and matrix factorization. Proficiency in Python (numpy, pandas, scikit‑learn, matplotlib) is essential, along with the ability to work with high‑dimensional datasets and implement interpretable models such as SHAP or feature‑importance methods. Familiarity with bioinformatics concepts like pathway scoring or RNA‑seq preprocessing is helpful but not mandatory, as long as the student is comfortable learning domain‑specific tools.

Research area: The HeART (Healthcare Applications for Robotic Technologies) Lab is focused on the fundamental understanding and development of magnetically actuated small-scale robots for surgical and on-chip applications. This involves investigating the design, fabrication, and control of these robotic devices. HeART Lab director Dr. Onaizah has been involved in the development of the first shape-forming magnetic continuum robots to navigate along tortuous anatomical paths and experimentally demonstrated this in realistic in vitro environments. The HeART works closely with clinicians to solve some of the most challenging problems at the forefront of medical robotics.

Student roles:
As a regular part of the team, the intern will be given a desk in one of the team offices. They will be integrated into a group of around 10 students working directly on medical robotics, under the direct supervision of Dr. Onaizah. In addition to this group, the intern will be a regular member of the HeART Lab, and benefit from its associated research ecosystem (e.g., seminars, interactions with other students, facilities). The intern will progressively take ownership of their project using a ramp-up approach. The first couple of weeks of the internship will be dedicated to understanding the research task ecosystem, and the different paths they can take in this context. Paired to a senior member of the team (PhD or Masters' student), careful analysis, reviews and guidance will be provided during the ramp-up period, so that eventually the intern can become more independent and can take initiative on their project. When reaching this point, an iterative and incremental approach will be used to tackle the research objective of this internship: a research question will be designed with their immediate supervisor, and the associated research will be done to tackle this question (including literature review, design analysis, experiments, coding, empirical analysis, when relevant to the research question). Finally, the obtained results will be presented to the other members during a group meeting, and the next steps and research directions will be discussed collectively

Skills required:
Minimum Requirements:
• Background in one of the following disciplines: Mechanical Engineering, Biomedical Engineering, Electrical Engineering, Robotics, Computer Science, or related disciplines
• A willingness to work effectively with staff, students, and visitors from a wide range of backgrounds.

Experience in one of the following areas would be an asset:
• Robot Operating System (ROS) and its Gazebo simulation package
• Object-oriented programming language (e.g., embedded C, Matlab, Python, C++, Java, or LabVIEW)
• Computer Aided Design software (e.g. Solidworks, AutoCAD)
• Computer Aided Manufacturing (3D printing, laser cutting, CNC milling)
• Magnetic Control or Manipulation
• Microfabrication

Research area: The research group focuses on the biomechanics of soft tissues through integrated experimental, analytical, and computational approaches. Mechanical characterization is performed using selected combinations of indentation, unconfined compression, uniaxial/biaxial tension, shear, and torsion tests, often complemented by microstructural information through micro-CT, multiphoton imaging, ELISA protein assays, and histology. Research activities also extend to constitutive modelling and advanced analytical, FEA, CFD, and FSI simulations that incorporate realistic material behaviour, geometries, and boundary conditions. Research goals range from improving data analysis and advancing fundamental understanding to developing diagnostic tools and evaluating treatments, with applications to healthy and pathological aortic and skin tissues.

Student roles:
All students will contribute to research dissemination through reports, abstracts, and presentations, and will participate in weekly group meetings and supervision or mentorship sessions.

In Stream 1, students will assist in developing testing protocols, conducting experiments, and analyzing mechanical and imaging data, including statistical interpretation.

In Stream 2, students will process and analyze complex experimental datasets, explore relationships between variables, and contribute to the development and evaluation of constitutive models.

In Stream 3, students will support the development of computational models, implement simulations, and analyze outputs to assess the effects of material heterogeneity on tissue mechanics.

Skills required:
All students are expected to participate in EDIA training and to demonstrate strong communication and teamwork skills. A solid foundation in mechanics (stress, strain, basic viscoelasticity) is required.

For Stream 1, experience with experimental work, including mechanical testing of tissues/materials and wet lab practices, is required; basic biosafety knowledge and exposure to statistics or imaging/DIC analysis are assets.

For Stream 2, familiarity with data analysis, optimization methods, and mathematical modelling is required; experience with constitutive modelling and FEA is an asset.

For Stream 3, background in constitutive modelling and finite element analysis is required; experience with Abaqus/UMAT is an asset.

Research area: In the Neurospine Lab at SFU we specialize in characterizing the mechanics of biological tissues and materials to better understand and model human injury. We have characterized a range of biological tissues from spinal cords through ribs and work to develop constitutive models to represent the different materials. We recently developed a new mechanical test system that will allow us to test the mechanics of biological tissues in the body. These tissue properties have largely been lacking from the literature, particularly at larger deformations and higher loading rates typical of injury.

Student roles:
The student will work with the graduate students and professor in the lab to conduct human subjects experiments to measure the mechanical properties of the soft tissues. The student will be trained in human ethics, mechanical testing and data analysis during the project. The student will assist in running the custom mechanical test system we have in the lab. In addition the student will learn ultrasound techniques and measure soft tissues with our wireless ultrasound system (Clarius). The student will also assist in measuring muscle activation using an EMG system and characterize the differences in soft tissue structures when the muscles are active and relaxed. The student will then work with the graduate students in the lab to analysis the results of the experiments and write a conference abstract and journal paper presenting the results.

Skills required:
The ideal student for this project is a senior student (> 3rd year) with a background in engineering or medicine with an interest in understanding, preventing or treating human injuries. The student must be able to work well with others and be able to conduct human subjects trials. Experience with mechanical testing systems (e.g. Instron, MTS, etc) is an asset but not required. Experience with finite element or rigid body dynamic modeling is also an asset but not required.

Research area: My specialized area is human movement research - more specifically biomechanics, motion capture, and gait analysis research. We use a 12 camera Vicon motion capture system to track body movement during walking. These infrared cameras track reflective markers located on the skin. Kinematic and kinetic data is computed and used to identify movement deviations. Biomechanical signals are processed using matlab, python, and Visual 3D software. This allows for a greater understanding of atypical and typical motion in the body. In turn, we can increase our understanding of disorders, treatments, and try to improve function and quality of life.

Student roles:
The student will be responsible for operating the motion capture system, collecting data, analyzing data, and using Visual 3D software for data analysis of biomechanical motion capture data. Students play an integral role in patient management and signal quality control during data collections. We have great student research teams here from all over the world - come join us!

Skills required:
Introductory biomechanics knowledge
Introductory Algebra/geometry knowledge (3D data)
Enjoy learning new software and hardware
Previous experience biomechanics data collection is not required, but would be an asset (motion capture will be taught to successful candidates)
Visual 3D experience is not required, but would be an asset (Visual 3D will be taught to successful candidates)
Python/Matlab programming not required, but would be an asset

Research area: My background is in the area of human factors and biomedical engineering. My specific research interests include neuromuscular physiology and rehabilitation of clinical populations as well as occupational physiology. The focus of my research is the use of surface electromyography (sEMG) to monitor muscle activity during movement. sEMG is the measurement of the electrical activity at the skin’s surface, that brings about muscle contraction. The sEMG can be used to examine muscle activation patterns for a variety of applications including improved prosthesis design (artificial limbs). Understanding neuromuscular function in movement efficiency will help develop better models of human movement.

Student roles:
The student will be responsible to work with the research team to collect and analyze data using high density EMG from those without physiological and/or neurological conditions as well as clinical participants. In addition, the student will use an isokinetic dynamometer to collect force data from both groups. The student will be responsible to integrate the testing equipment and analyze the resulting data in order to compare the movement patterns between those who have movement limitations and those without. In addition, the student may be required to generate software code to assist with the data analysis. Currently there are many different algorithms that are used to process high density EMG data including pattern classification methods and machine learning algorithms. The student will need to be able to understand these algorithms, develop software utilizing these algorithms and ensure data validity.

Skills required:
The student should have a background in electrical and/or biomedical engineering with a strong interest in human movement. The skills required include familiarization with data acquisition equipment and hardware as well as strong programming skills (Matlab and/or Python). In addition, the student should have strong mathematical skills and the ability to critically analyze published research. The intern must also have a strong background in EMG signal processing and software development including pattern recognition and machine learning algorithms. The student should also have strong communication skills.

Research area: Our research focuses on the neuronal substrates involved in the control of human locomotion. Areas of expertise include neuroscience, motor control, computer science and engineering. We use techniques such as movement kinematics, EMG, neuronavigated brain stimulation (TMS) and brain imaging with MRI and PET. We study human locomotion both overground and on a split-belt treadmill.

Student roles:
Develop analysis pipelines to facilitate the integration of our multimodal data. Collaborate with graduate students to conduct the project.

Skills required:
Interest in the analysis of biological signals. Interest in teamwork.

Research area: I am the principle investigator of Laser Nano Micro Manufacturing Laboratory (LNMM)at TMU. My group develops advanced nano materials for biomedical detection. My group has made significant contribution to the diagnosis of cancer using Surface Enhanced Raman Sensing of human blood samples. Our recent findings were published by Nature Communications, Advanced Science and others.

Student roles:
We are looking for highly motivated undergraduate students to join us to assist us with laboratory research in the abovementioned direction. Student will work with a Ph.D candidate or a postdoctoral fellow. She or he will be trained to operate analytical instrument, such as SEM, TEM , AFM, EDX, and use Mechine Learning tools for statistical anlaysis . Main tasks in the lab will include assisting with experiments and collecting data, preparing plots charts, and conducting literature research. Student will document data and provide a report at the end of the project.

Skills required:
. This a multidisciplinary research area. Student with material engineering, biology and medical science background suits the best for the position. However, student from all engineering disciplines could benefit from this project. Must be familiar with Microsoft office tools. Photoshop editing skill is a plus. Must be cultural sensitivity and able to work in a diverse workforce in multicultural settings.

Research area: Our lab is dedicated to understanding how therapeutic neuromodulation (deep brain stimulation/DBS and focused ultrasound FUS) affects the brain in humans and animal models. We also focus on developing neural prostheses with the basic science underlying these. This means that we study human physiology using intra-operative microelectrode recordings, post-operative EEG, functional MR imaging and transcranial magnetic stimulation (TMS) in patients with DBS systems. We also study the fundamental mechanisms of DBS and FUS in animal models in vivo and in vitro using electrophysiology, optical imaging, fibre photometry.

Student roles:
The student(s) will participate in data collection and analysis under the direct supervision of a graduate student or post-doctoral fellow. S/he will write a report on their project and present their results at our summer student symposium in August (previous MITACs students have presented at the Alberta Motor Control meeting if they start later and stay until mid-Sep). Students will attend the operating room in order to gain experience with patients undergoing DBS surgery. However the majority of their time is spent in data analysis and experiments in the lab.
1. Human brain oscillations: For this project, the summer student will participate in data collection in the operating room with the rest of the team. Most of their time will
be spent digitizing the data we collect, pre-processing it, and analysing the local field potentials and cellular firing patterns. Depending on the student’s background they may write code to do this, or may use previously written Matlab or Python code.
2. DBS and plasticity of neural circuits: The student will help the postdoctoral fellow with data collection and analysis of TMS measures in patients with DBS systems. This will
include using Matlab scripts and may involve writing code or further programming.
3. Imaging and DBS in rodent models: Again depending on the student’s background, they may participate in characterizing the instrumentation, implanting it into the rodents, or analyzing the imaging, electrophysiological or behavioural data generated.
4. Focused ultrasound neuromodulation: The student will work others in the lab performing experiments in rat brain slices measuring local field potential, applying FUS to the
slices. The student may also participate in ongoing human experiments of low intensity neuromodulatory FUS.

Skills required:
Senior undergraduate level engineering / physics or physiology are ideal. Those with a neuroscience background or experience in electrical biomedical engineering are preferred.

Research area: My research focuses on ultrasound technologies for medical imaging and therapeutic applications. We develop tools that connect acoustics, mechanics, materials, electronics and signal processing to design new probes, improve image formation and guide non-invasive interventions. Applications include high-resolution imaging, focused ultrasound, transcranial approaches and the experimental characterization of ultrasound systems.

Student roles:
The intern will play an active role in the development and validation of ultrasound technologies for medical imaging and therapeutic applications. Depending on their background, they may contribute to the design of an experimental setup, the fabrication or integration of probe components, ultrasound data acquisition, signal processing, image formation or the analysis of experimental results.

They will be encouraged to take part in all stages of the project: targeted literature review, definition of technical objectives, selection of methods, experiments, critical data analysis and presentation of results to the team. The internship will provide an opportunity to connect concepts from engineering, physics and biomedical sciences to concrete healthcare applications.

The selected student will work in collaboration with graduate students and laboratory members, while progressively developing independence. They will be expected to document their work carefully, share progress during team meetings and contribute to the advancement of an interdisciplinary research project. The role can be adapted to highlight the student’s strengths and support the development of new skills.

Skills required:
Desired background: student in engineering, physics, applied sciences, computer science or a related field, with strong curiosity for biomedical technologies. Experience in scientific programming, signal processing, electronics, mechanical design, acoustics, materials, simulation or experimental work is an asset. The student does not need to master all these areas: we are mainly looking for someone rigorous, independent, creative, able to work in a team and motivated to learn in an interdisciplinary environment.

Research area: Dr. Tan is an Affiliate Scientists at Li Ka Shing Knowledge Institute, a part of Unity Health Toronto. She has been working in the field of ultrafast laser Nano material synthesis and application for about two decades. To her credit, she published over 150 high impact factor journal publications and several patents. Dr. Tan’s research group has made significant contribution to the research in designing nanomaterial for multiple bio-based applications. Her most recent research focuses on nano sensors detection of blood-based biomarkers for disease diagnosis.

Student roles:
Student will work with a Ph.D candidate or a postdoctoral fellow. She or he will be trained to operate analytical instrument, such as Raman Microscopy and use machine learning tools for statistical analysis. Main tasks in the lab will include assisting with experiments and collecting data, preparing plots and charts, generate scientific illustrations for manuscripts and conducting literature research.

Skills required:
This is a multidisciplinary research area. Students with a biology background suits the best for the position. However, students from all engineering disciplines could benefit from this project. Must be familiar with Microsoft office tools. Photoshop editing skill is a plus. Must be culturally sensitive and able to work in a diverse workforce in multicultural settings.

Research area: Biomedical engineering, biometrics, biometric signal processing, machine learning, probabilistic causal models, wearable devices, decision support

Student roles:
The student will work both individually and in a team of other 4-6 research students 4-5 days a week in a university research lab on campus.
The student will be provided with a desktop computer and computer desk.
The student will work with a lab software to perform synthesis and analysis on the data, already collected in the lab or simulated/generated.
The student will meet with the supervisor 3-4 times a week, participate in lab seminars, present at some seminar, and write reports (2-3 times per term).

Skills required:
Basic biomedical or electrical engineering knowledge, signal processing, basic statictics, Python and related software

Research area: Dr. Albert Vette is currently a tenured Full Professor in the Department of Mechanical Engineering, University of Alberta, and a Research Scientist at the Glenrose Rehabilitation Hospital, Edmonton, Canada. His work is centered at the interface between musculoskeletal biomechanics, neuromuscular control, and rehabilitation engineering. Particular research interests of Dr. Vette include the dynamics and control of neuromuscular processes; the modeling of physiological systems; sensory-motor integration during human movement and posture; and assistive technology for individuals with neurological disorders.

Student roles:
The trainee's first task will be to synchronize the smart wobble board with other experimental equipment and develop a software interface allowing the user to store and visualize the acquired tilt angles on a tabloid. The second task will be to complete the validation experiments and assess the degree of correlation between the wobble board and motion capture measurements. Finally, she/he will perform the experiments in the two sample populations, compute the mechanistic quantitative measures, and identify age differences. By performing this work, the trainee will gain valuable practical experience on a clinical research question. In particular, she/he will be able to apply her academic knowledge to a clinical problem, but also learn to overcome technical and experimental hurdles not present in the academic environment. She/he will work in a highly interdisciplinary environment at our laboratory. She/he will benefit from a wide range of expertise, learn about clinical challenges and needs, and develop a professional network that will be beneficial in the future. We plan to publish the work in a clinically relevant journal, which will allow the trainee to gain valuable writing experience and further strengthen her resume.

Skills required:
The student should have a background in Mechanical, Computer, Electrical, or Biomedical Engineering - or any related field. Students should have some knowledge in data acquisition, processing and analysis, and be generally interested in the biomechanics research field.

Research area: This research area focuses on the design and optimization of ultrasound transducers for use in ultrasound bioeffects research coupled with optical microscopy. It combines acoustics, finite-element modelling, and experimental instrumentation to enable precise, controlled ultrasound exposure while preserving high‑resolution optical access. Using computational tools such as COMSOL Multiphysics, the work explores how transducer geometry, acoustic performance, and physical constraints interact in integrated imaging systems. The research also addresses practical considerations in fabrication and mechanical design, supporting robust experimental implementation at the intersection of biomedical engineering, ultrasound physics, and microscopy-based biological research.

Student roles:
The student will take a central role in the computational design and engineering optimization of a ring‑shaped ultrasound transducer for microscopy‑based bioeffects research. Drawing on a background in acoustics, biomedical engineering, physics, or related disciplines, the student will develop and iterate finite element models to evaluate acoustic performance under strict spatial and optical constraints. They will analyze simulation outputs using appropriate data analysis tools to inform design decisions and refine transducer geometries. The student will also investigate feasible fabrication approaches and contribute to the design of a functional casing that supports alignment, integration, and experimental robustness. Throughout the project, the student will clearly document methods and results while applying problem‑solving and iterative design skills in an interdisciplinary research environment.

Skills required:
Suggested student background:
• Background in acoustics, biomedical engineering, physics, mechanical or mechatronics engineering
• Preferred experience with numerical simulation or finite element modelling (COMSOL preferred, but not required)
• Basic data analysis and interpretation skills (e.g., MATLAB, Python, or similar tools)
• Understanding of experimental constraints and design requirements
• Interest in biomedical imaging, ultrasound, or biophysical effects
• Strong problem-solving skills and willingness to iterate on designs
• Ability to document technical work clearly

Research area: The project is intended as research towards autonomous robotic surgery. Robot learning enables systems to acquire manipulation skills directly from expert demonstrations rather than hand-coded rules. In surgical robotics, such demonstrations can be obtained from robotic systems interacting with phantom tissue models and simulated environments approximating microsurgical conditions. The internship focuses on abstracting a small set of representative, repeatedly performed surgical subtasks, collecting demonstration datasets for them, and testing and adapting state-of-the-art imitation-learning algorithms, such as Action Chunking Transformer and related policies, to learn these surgical skills from data.

Student roles:
Over the four-month internship, the student will work through a complete robot learning pipeline for surgical skill acquisition, from task definition to dataset collection to algorithm testing, under the close guidance of faculty and graduate researchers.

In the first month, the intern will be onboarded to the experimental setup, existing codebase, and data formats, and will work with the supervisory team to identify and abstract two to three surgical subtasks that are repeatedly performed in practice, such as precision targeting, curved path following, or soft tissue manipulation. For each task, the intern will help define clear start and end conditions, success criteria, and a simple protocol for repeatable demonstration trials.

In the second month, the intern will support supervised data collection sessions on phantom tissue models and in simulation for each abstracted task, and will implement scripts to record, clean, and synchronize multimodal data streams, including robot kinematics, tool state, and, where available, force/torque and camera observations, organizing them into a standardized, labeled dataset.

In the third month, the intern will set up and run existing implementations of state-of-the-art imitation-learning algorithms, such as Action Chunking Transformer and comparable behavior-cloning baselines, training them on the collected dataset for each task and beginning basic performance evaluation.

In the final month, the intern will refine and compare algorithm performance across tasks, contribute to simple visualization tools for replaying trajectories and learned behavior, and document the pipeline, dataset, and results in a final report and presentation. This internship offers hands-on, supervised experience spanning surgical robotics, dataset engineering, and applied robot learning.

Skills required:
The ideal candidate is an undergraduate student in mechanical engineering, electrical engineering, computer engineering, biomedical engineering, computer science, or a related field. Working knowledge of Python is required, and familiarity with PyTorch or another deep learning framework is a strong asset. Coursework or project experience in robotics, machine learning, or data analysis is beneficial. Prior exposure to ROS, sensor data, or computer vision is helpful but not required. The student should be organized, detail-oriented, and comfortable running and adapting existing code under supervision. Curiosity about robot learning, surgical robotics, and imitation learning is highly desirable.

Research area: Biometrics, biometric signal processing, biomedical engineering, probabilisitc causal models, machine learning and machine reasoning, decision support, wearable devices

Student roles:
The student will work both individually and in a team of other 4-6 research students 4-5 days a week in a university research lab on campus.
The student will be provided with a desktop computer and computer desk.
The student will meet with the supervisor 3-4 times a week, participate in lab seminars, present at some seminar, and write reports (2-3 times per term).

Skills required:
Experience with a high‑level programming language (Python, JavaScript, or similar); Introductory course in networking or web development is an asset.
Basic understanding of cryptographic concepts (encryption, authentication) is helpful but not mandatory.

Research area: Smart textiles can sense changing conditions, perform actions, and adapt their performance to the environment. When integrated into garments, they allow harnessing the large contact surface area with the skin to provide a multitude of sensing and actuating functions to the garment. Current applications including monitoring vital signs and providing heat and cooling to the body. Smart garments’ potential impact in the medical field is immense. They will be a life changer for people benefitting from it.

Student roles:
The mission of the student in this project includes the following tasks:
• Prepare an experimental design
• Produce samples
• Characterize the sample performance
• Analyze the results
• Produce technical reports
• Prepare progress presentations

The intern will be trained on different manufacturing and characterization techniques relevant to sensors and actuators, and their integration into a garment in the state-of-the-art laboratories at the University of Alberta. The student will work in close collaboration with the Master, PhD, and other undergraduate students in the team. Throughout the internship, they will also interact with the different researchers in the team, whose expertise cover the different aspects of the question: neuroscience, textile science, materials engineering, biosensors, electrical stimulation, machine learning, evidence-supported decision, and technology validation.

Skills required:
In addition to solid skills in electrical, materials, and/or textile engineering, it is critical that the student is curious and rigorous, as well as open to interdisciplinary research. They should be able to work independently while displaying a good ability for teamwork. Oral and written communication skills are important as well. Some lab work experience, for instance at preparing samples and characterizing their performance, as well as prior experience in being part of studies with human participants are an asset.

Research area: Biomedical engineering, biometrics, biometric signal processing, machine learning, probabilistic causal models, wearable devices, decision support

Student roles:
The student will work both individually and in a team of other 4-6 research students 4-5 days a week in a university research lab on campus.
The student will be provided with a desktop computer and computer desk.
The student will work with a lab software to perform synthesis and analysis on the data, already collected in the lab and simulated/generated.
The student will meet with the supervisor 3-4 times a week, participate in lab seminars, present at some seminar, and write reports (2-3 times per term).

Skills required:
Basic biomedical or electrical engineering knowledge, signal processing, basic statictics, Python and related software

Research area: Our lab’s main interest is to decipher the anatomical and functional logic of neural circuits linking sensory and motor regions of the brain to reveal how they help orchestrate the production and learning of skilled movements in mice. By gaining a better understanding of these fundamental mechanisms, our ultimate goal is to develop new therapeutic treatments for neurodevelopmental disorders and acquired brain injuries linked to sensorimotor deficits such as cerebral palsy, autism, traumatic brain injury and stroke.

Student roles:
Depending on the student's background and interests, the role of the student may encompass:
- Building an automated food delivery system (designing hardware for 3D printing, developing microcontroller firmware to manage peripheral electronics, synchronizing video recordings with trial start)
- Programming AI algorithms to classify trial outcome
- Programming machine learning data analysis pipelines for movement analysis using DeepLabCut
- Creating unified data storage framework
- Programming user interfaces
- Training mice on motor task
- Creating scientific figures
- Researching the literature

Skills required:
We are looking for highly motivated students from the fields of biomedical, electrical, mechanical or computer engineering or from the fields of neurosciences, biomedical sciences or rehabilitation with coding skills (Python or Matlab), knowledge of machine-learning and/or robotics skills. Students should have good communication skills and the ability to work effectively within a team.
Candidates with diverse skills and career objectives will be considered.

Research area: Dr. Albert Vette is currently a tenured Full Professor in the Department of Mechanical Engineering, University of Alberta, and a Research Scientist at the Glenrose Rehabilitation Hospital, Edmonton, Canada. His work is centered at the interface between musculoskeletal biomechanics, neuromuscular control, and rehabilitation engineering. Particular research interests of Dr. Vette include the dynamics and control of neuromuscular processes; the modeling of physiological systems; sensory-motor integration during human movement and posture; and assistive technology for individuals with neurological disorders.

Student roles:
The first component of this project, i.e., the development of the torque sensor device, is currently underway. Our expectation is to have a final design of the sensor device by the end of summer 2026 and prototyped by the end of fall 2026. In light of this, the Globalink 2027 student will work on the second component of the project. Specific tasks are:

(1) To integrate the torque sensor device in an experimental setting (that includes electromyography measurements) to validate the torque readings and ensure smooth and accurate data transmission/acquisition.
(2) To perform experiments in healthy individuals to acquire the necessary biomechanical and electrophysiological data for quantifying the torque generation delay.
(3) To process and analyse the acquired data to characterize the relationship between muscle activation (amplitude and frequency) and torque generation. Data will be analyzed in terms of time delay magnitude and muscle-fatigue resistance.

Note that all work will be performed under continuous supervision and guidance from Dr. Vette and his graduate students.

Skills required:
Preferably, applicants should have a background in Mechanical, Computer, Electrical, or Biomedical Engineering (or related field). Students should have some knowledge in device development, data acquisition, processing and analysis, and be generally interested in the biomechanics research field.

Research area: Biomedical engineering, biometrics, biometric signal processing, machine learning, probabilistic causal models, wearable devices, decision support

Student roles:
The student will work both individually and in a team of other 4-6 research students 4-5 days a week in a university research lab on campus.
The student will be provided with a selection of research-grade low-power wearable sensor components, desktop computer and computer desk.
The student will work with a lab software to perform signal processing and analysis on the data, already collected in the lab or simulated using signal generators.
The student will meet with the supervisor 3-4 times a week, participate in lab seminars, present at some seminar, and write reports (2-3 times per term).

Skills required:
Basic biomedical or electrical engineering knowledge, signal processing, basic statictics, Python and related software

Research area: Our research group specializes in flexible, wearable electrochemical biosensors for continuous, non-invasive hormone monitoring in biofluids such as sweat, interstitial fluid, and saliva. We work at the intersection of materials science, microfluidics, and bioanalytical chemistry, integrating affinity-based biorecognition elements with advanced electrode architectures to achieve clinically relevant sensitivity in complex matrices. Detection strategies, including differential pulse voltammetry and electrochemical impedance spectroscopy, are translated into miniaturized, wireless-compatible formats for real-time, longitudinal monitoring outside laboratory settings. Current projects target hormones involved in stress response, reproductive health, and metabolic regulation, advancing personalized point-of-care health management through wearable sensing technologies.

Student roles:
• Design and fabricate flexible electrochemical sensor arrays functionalized with hormone-specific aptamers or antibody conjugates.
• Optimize surface chemistry and electrode modification strategies to achieve clinically relevant detection limits (pg/mL to ng/mL range).

Skills required:
• Background in Chemistry, Biochemistry, Biomedical Engineering, or related field
• Experience with laboratory wet chemistry and surface functionalization
• Knowledge of immunoassay methods (ELISA)

Research area: Biomedical engineering, biometrics, biometric signal processing, machine learning, probabilistic causal models, wearable devices, decision support,

Student roles:
The student will work in a team of other 4-6 research students 4-5 days a week in a university research lab on campus.
The student will be provided with a selection of research-grade low-power wearable sensor components, desktop computer and computer desk.
The student will work with a lab software to perform signal processing and analysis on the data, already collected in the lab or simulated using signal generators.
The student will meet with the supervisor 3-4 times a week, participate in lab seminars, present at some seminar, and write reports (2-3 times per term).

Skills required:
Basic biomedical or electrical engineering knowledge, embedded design knowledge, signal processing, basic statictics, Python and related software