Engg-Aeronautical

Research area: My area of research covers spacecraft design, scientific instrumentation, planetary rovers, space manipulators, planetary drills, in-situ resource utilisation, 3D printing

Student roles:
The student will be using a variety of 3D printing machinery

Skills required:
Enthusiasm and a problem-solving attitude. This is a challenging problem and requires the ability to think beyond your normal comfort zone. CAD and practical skills are essential.

Research area: The main research topic of our group is the study and characterization of complex unsteady flow phenomena with multiphysics couplings, including aeroacoustic, and aeroelastic phenomena. Such phenomena are present at multiple scales, from large, high-speed rotating machines (high-speed turbomachines and low-speed fans) to small biological flows (flexible cilia in the brain ventricles and choroid plexus). Understanding these phenomena is needed to help design more efficient, quieter, and safer systems. The lab (established in 2025) focuses on modelling such phenomena using (1) high-fidelity CFD simulations, (2) low-order analytical models, and (3) AI data-driven surrogates taking advantage of the other two approaches.

Student roles:
The intern will take ownership of the day-to-day development and analysis of the machine-learning pipeline, from signal preprocessing through model evaluation and critical interpretation of results, working under regular supervision. The role combines scientific modelling, signal-processing, and acoustic analysis.

Concretely, the student will:
(1) Become familiar with the fundamentals of fan aerodynamics and stall, acoustic condition monitoring, and anomaly-detection methods
(2) Set up a preprocessing chain (segmentation, filtering, time-frequency, and spectral feature extraction) and establish simple baseline detectors
(3) Training and tuning candidate models such as autoencoders and other unsupervised detectors, testing with appropriate metrics (precision, recall, ROC/AUC) given rare anomalies,
(4) Comparison between ML models and baselines
Throughout, the student will document the methodology, maintain clean and version-controlled code (git), and present progress in regular meetings, culminating in a written report and an oral presentation.

Skills required:
The ideal candidate is a student in engineering, computer science, physics, or a related field, with programming experience (Python preferred) and an interest in signal processing or acoustics; prior exposure to machine learning helps, but a willingness to learn rigorously matters more.
He/She should be able to read scientific publications to implement existing ML models.

Research area: Our research focuses on the autonomous relative navigation of spacecraft flying in formation. Space missions increasingly rely on satellite groups (such as a chief and deputy satellite) working cooperatively. To maintain precise formations, these satellites must continuously estimate their relative positions, often using Global Navigation Satellite System (GNSS) distance measurements. Because sensor faults and environmental disturbances can corrupt this data, we design advanced mathematical tools, such as adaptive filters, that allow spacecraft to detect errors and dynamically adjust to maintain accurate navigation. We also utilize hardware-in-the-loop platforms to validate these cooperative algorithms using physical laboratory setups before deployment in orbit.

Student roles:
Depending on their specific background, the student will focus on either algorithm simulation or hardware-in-the-loop testing for satellite relative navigation.

- Students focusing on simulation will write code to test estimation algorithms and relative motion models. They will run these computer models to evaluate how accurately the simulated spacecraft can coordinate their movements and maintain precise relative state estimation in the presence of injected measurement faults.
- Students working on the hardware-in-the-loop platform will spend more time in the lab, setting up physical equipment, interfacing sensors, and running controlled physical experiments. They will collect and analyze data to understand how the adaptive software performs when connected to the hardware.

All students will review introductory literature on Kalman filtering and space navigation, participate in regular team meetings, and contribute to improving the robustness of the multi-spacecraft navigation models.

Skills required:
The student should be studying engineering, computer science, or related fields. They must be comfortable with applied mathematics and have experience writing algorithms (using tools like MATLAB, Python, or C++). For students focusing on software, a foundational understanding of estimation theory, orbital mechanics, or control systems is highly desirable. For students interested in the hardware testing side, experience with electronics, hardware-in-the-loop setups, or microcontrollers is highly beneficial. A strong willingness to solve complex problems within a team environment is essential.

Research area: Hybrid fiber composites employ multiple fiber reinforcement materials to obtain mechanical performance beyond the capabilities of conventional single-fiber reinforcement. By combining the high strength of carbon fibers with the strain-resilience of glass fibers in a hybrid fiber composite, thin-shell structures can be optimized for enhanced bending capabilities where conventional carbon fiber thin shells fail. Such compliant mechanisms are effective in applications requiring strict packaging efficiency, where large volumes must bend or fold into compact spaces and subsequently expand upon deployment, for example in deployable booms, hinges, and origami structures.

Student roles:
As a Mitacs Globalink summer researcher, the student will contribute to the design and testing of a demonstrator of a hybrid composite compliant thin-shell component.

The primary responsibility will be to devise and implement strategies for systematically optimizing hybrid fiber architectures using simulated and experimental material data for the component demonstrator.

Additional responsibilities will include the following:
· Developing supplementary analytical and numerical modeling tools to apply constituent and hybrid material data in component-level finite element models.
· Fabricating composite specimens and a component demonstrator using advanced additive manufacturing equipment with precise control over fiber placement.
· Conduct mechanical testing to evaluate component performance.
· Design fixtures to emulate component-level load cases.
· Collect, analyze, and interpret experimental data with rigor and attention to detail.
· Document procedures, results, and analyses thoroughly in technical reports.
· Present findings in meetings and contribute to academic publications and presentations.

Skills required:
This project is suitable for students pursuing a degree in mechanical engineering, materials engineering, aerospace engineering, or a closely related discipline. Preference will be given to candidates nearing completion of their degrees.

Candidates must demonstrate strong attention to detail and a commitment to producing reliable and reproducible results.

Skills and experience with the following are preferred:
· Proficient with computer-aided design (SolidWorks or similar)
· Experience with basic finite element modeling (ABAQUS, ANSYS, SolidWorks Simulation)
· Experience with data acquisition and digital modeling (Python, MATLAB) is highly desirable.
· Experience with fused filament fabrication (FFF) additive manufacturing is a plus.

Research area: Deployable structures are widely used in satellites and space systems to minimize launch volume and mass. Once in orbit, these structures unfold to their operational configuration. A prominent example is deployable antenna reflectors used for satellite communications. At the same time, recent advances in materials and additive manufacturing enable lightweight, high-performance structures with increased design flexibility.

Student roles:
As a Mitacs Globalink Summer Researcher, the student will contribute to the design, development, and testing of deployable antenna reflector concepts.

Responsibilities include:
- Conducting literature reviews and technology surveys.
- Developing deployment mechanism and hinge concepts.
- Creating CAD models and engineering drawings.
- Supporting additive manufacturing and prototype fabrication.
- Participating in experimental testing and design evaluation.
- Analyzing results and comparing designs against established requirements.
- Preparing technical reports and presentations summarizing project outcomes.

Skills required:
This project is suitable for students pursuing a degree in Mechanical Engineering, Materials Engineering, Aerospace Engineering, or a related discipline. Preference will be given to students in the later stages of their degree.

Required qualifications:
- Strong interest in design, manufacturing, and space applications.
- Ability to work independently and systematically.
- Excellent attention to detail.
- Strong written and verbal communication skills in English.


Assets:
- Experience with CAD software and engineering design.
- Experience with additive manufacturing.
- Experience with prototyping and experimental testing.

Candidates from underrepresented groups are strongly encouraged to apply.

Research area: My research field focuses on aerodynamics and fluid mechanics applied to ventilation systems. The objective is to understand turbulence phenomena and boundary layer separation at the blade scale to improve energy efficiency. We use standardized airflow test benches to measure overall performance (pressure, flow rate, power) while integrating advanced measurement techniques. The challenge is to reconcile high air transfer performance with a drastic reduction in noise pollution, thereby meeting current environmental standards.

Student roles:
The student will lead the numerical simulation and aerodynamic optimization pipeline for the DRS project. Their role is multidimensional and structured around the following core tasks:CAD & Mesh Generation: The student will adapt 3D CAD models of the 2026 F1 rear wing assembly (open and closed configurations) and generate high-quality computational meshes, ensuring strict grid refinement in the boundary layer and wake regions.CFD Simulation Execution: They will set up and run steady-state and transient CFD simulations. This involves defining appropriate moving ground and rotating wheel boundary conditions, selecting advanced turbulence models, and ensuring solution convergence at various angles of attack.Aerodynamic Analysis: Using post-processing tools, the intern will extract critical performance metrics, such as drag and lift coefficients ($C_d$, $C_l$), and analyze vortex shedding, recirculation zones, and pressure distributions over the wing profiles.Optimization & Reporting: The student will propose geometric modifications to optimize the DRS efficiency based on their numerical findings. They will document the methodologies and results in comprehensive technical reports and present progress during weekly laboratory meetings.

Skills required:
The ideal candidate should be a student in Aerospace, Automotive, or Mechanical Engineering. A strong theoretical background in external aerodynamics, turbulence modeling (RANS/LES), and boundary layer control is essential. Proven experience with high-fidelity CFD software (e.g., ANSYS Fluent, Star-CCM+, or OpenFOAM) and advanced mesh generation for complex geometries is required. Proficiency in Python or MATLAB for processing aerodynamic coefficients, lift/drag ratios, and flow visualization is highly desirable. Passion for motorsport engineering and a strong capacity for autonomous scientific research are major assets.

Research area: - Aerodynamics of drones - Pixelated wind facilities (windshapers), which are wind tunnels that can generate real winds and meteorological conditions for developing, testing, or certifying drones - Ground-effect drones - Drones that can harvest atmospheric turbulence to stay aloft indefinitely - Drones in arctic conditions

Student roles:
- program the operation of windshapers
- measure winds with various tools (multihole probes, hot wire)
- design and fabricate drones or drone elements
- mount drones on collaborative robotic arms
- instrument the drone with force measuring equipment (force balance)
- measure aerodynamic forces
- design a system capable of generating icing clouds in front of windshapers

Skills required:
- at ease with manual work
- at ease with work in a laboratory environment
- CAD design
- basics of aerodynamics

Research area: Our research focuses on how spacecraft can safely navigate and operate in cislunar space. Spacecraft need to be able to figure out where they are, understand their surroundings, and track hazards without always waiting for instructions from Earth. We design the mathematical tools, navigation algorithms, and computer programs that allow spacecraft to make these decisions safely and autonomously.

Student roles:
The student will help build and run computer simulations of spacecraft orbiting the Moon. They will test how well different mathematical formulations can predict a spacecraft's location or track other objects in space. Day-to-day tasks will include running these simulations, collecting the output data, and creating graphs or reports to show how accurate the navigation tools are under different conditions. The student will also read introductory papers on space navigation to understand the background of the project. They will meet regularly with the research supervisor and the team to discuss their findings, troubleshoot code, and brainstorm ways to improve the navigation models.

Skills required:
The student should be studying engineering, computer science, or related departments. They need to be comfortable with math and have some experience writing computer code (such as Python, MATLAB, or C++). An interest in space exploration and how things move in space (orbital mechanics). A willingness to learn and solve problems.

Research area: I study the multidisciplinary design optimization of blended-wing-body aircraft using computational fluid dynamics and numerical optimization. I use a supercomputer to automate the design optimization process while considering aerodynamics, mass properties, propulsion, and flight mechanics, and I uncover novel design principles for the unconventional, complex, and highly integrated blended-wing-body aircraft configuration.

Student roles:
Students will assist in the implementation of mission variables, and liquid-hydrogen fuel and boundary-layer-ingesting engine models within a conceptual aircraft-design code and, once tested, transfer them to the high-fidelity supercomputer code. The goal is to produce a good initial estimate and a refined optimal BWB aircraft design, which credibly exploits future technologies. This design will subsequently be refined using the supercomputer-based approach described previously, i.e., the design will be further optimized using computational-fluid-dynamics-based aerodynamic performance calculations and coupled models of aircraft weight and balance, propulsion, and flight mechanics. Finally, key design principles will be identified and published in the scientific literature.

Skills required:
The student requires skills in mechanical engineering (especially fluid mechanics), engineering design, and scientific programming (mainly Matlab; any other languages used have a similar syntax).

Research area: I am working in the area of applied analysis with emphasis on partial differential equations in engineering and environmental/natural applications. Many projects are inter-disciplinary, involving theoretical concepts of mathematics, fluid mechanics, thermodynamics and control theory, but also numerical solutions to problems intractable analytically and optimisation problems. I partner with local industry, engineers, consultants and other faculty to share field data and provide insight into problems encountered in different disciplines. The research is oriented at improved engineering design or computation, educated decision making and deep understanding of the physics of the systems at hand, especially ones combining natural and man-made components.

Student roles:
The expected outcomes are
(a) written analysis (verbal synopsis and mathematical derivations) of the
functionality of a set of filtering techniques indicated by Dr. Gonzalez
(b) filtering of signals (brain wave data acquired at Dr. Gonzalez's lab)
by different techniques
(c) interpretation and comparison of modelling outcomes for different
filtering methods
(d) identification of filtering parameters that significantly affect
outcomes
(e) a parametric scan of the above and analysis of the resulting
variability in modelled variables, i.e. a sensitivity / error analysis
(f) code in MATLAB/Octave
(g) written report.

The students will
(a) get acquainted with existing filtering techniques
(b) write up a synopsis of mathematical concepts and implementation thereof
(c) examine provided data
(d) get acquainted with the code in its current state (MATLAB/Octave)
(e) get acquainted with the current state of research by reading scientific
papers
(f) work on code and signal analysis with the aim to identify key
differences between filtering techniques, possible artefacts, relevant
parameters etc.
(g) juxtapose results to the magnitude of related quantities reported in
the literature
(h) based on the above, run a parametric scan and collate outcomes
(j) share findings with colleagues at all stages of the project
(i) write a final report.

Skills required:
(a) concepts in signal analysis: Fourier transform, smoothing techniques,
filtering
(b) fluency in MATLAB/Octave, including reading documentation of core
functions and new toolbox functions
(c) excellent data visualisation and writing abilities (no AI or other
aids); please, upload a technical writing sample (academic report or
similar) with your cv (single pdf file).

Background: engineering (mechanical/aeronautical), physics/biology,
mathematics, computer/data science or combination of these disciplines.
Note: pure computer/data science background is not sufficient, the intern
must have a physics/biology/applied math/engineering background to some
extent.

Research area: My research field focuses on aerodynamics and fluid mechanics applied to propellers. The objective is to understand turbulence phenomena and boundary layer separation at the blade scale to improve energy efficiency. We use standardized airflow test benches to measure overall performance (pressure, flow rate, power) while integrating advanced measurement techniques. The challenge is to reconcile high air transfer performance with a drastic reduction in noise pollution.

Student roles:
The student will be the primary researcher responsible for the numerical simulation pipeline. Their role is multidimensional and includes the following key tasks:

Geometry and Mesh Generation: The student will set up the 3D CAD models of the drone propellers and design high-quality, unstructured or hybrid computational meshes, with specific attention to the boundary layer grid refinement near the blades and the ground.

CFD Simulation Setup: They will define appropriate boundary conditions, select suitable turbulence models (e.g., RANS, LES), and run steady-state and transient simulations representing crosswinds, ground effects, and obstacle wakes.

Data Post-Processing and Analysis: The student will analyze the flow fields, extracting critical aerodynamic coefficients (thrust, power, torque) and visualizing vortex structures using Python, MATLAB, or Tecplot.

Reporting and Collaboration: The intern will document the methodology and findings in technical reports, present progress during weekly lab meetings, and collaborate with the team to discuss how these numerical insights can impact drone control and design.

Skills required:
A strong theoretical background in fluid dynamics (Navier-Stokes equations, turbulence modeling) is essential. Proven experience with commercial or open-source CFD software (such as ANSYS Fluent, Star-CCM+, or OpenFOAM) and mesh generation tools is required. Basic programming skills in Python or MATLAB for data post-processing are highly desirable. The student should demonstrate strong analytical skills, autonomy, and the ability to communicate scientific results effectively in a collaborative research environment.

Research area: About 10% of aircraft accidents are related to meteorological phenomena such as icing. Ice accretions on aircraft reduce lift and increase drag. An aircraft can be certified to fly in icing conditions if it has proper ice protection systems. The design challenge is to use the less energy possible to heat the surface while keeping the aircraft safe to fly in icing conditions. De-icing systems require less energy than anti-icing system, but aircraft must fly with small amount of ice. It is thus important validate existing numerical method to predict the airfoil performance when ice accretes on the wing.

Student roles:
The trainee will participate in the development of a data base to predict the aicraft performance degradation caused by ice accretion. The trainee must first get familiar with the relevant scientific literature. According to the skills and interests of the student, its tasks may include:
1. Build meshes on academic geometry relevant to icing problem;
2. Analyse data with visualisation software;
3. Do parametric studies;
4. Disseminate the results using scientific reports.

Skills required:
Good knowledge of viscous fluid mechanics (external flow) and heat transfer is essential. The ability to read and write technical reports in English is necessary. The trainee must have an interest in numerical simulation and programming.

Experiences with mesh generation tools, Computational Fluid Dynamics (CFD), flow visualization software are required and super-computing tools will be helpful.

Research area: Wind turbine rotors can encounter icing condition. This create ice accretion on rotor blade, reduce the lift and increase the torque. However, vibration and centrifugal forces remove ice accretions if they are large enough. The prediction of ice accretion on blades is challenging because of the unsteady nature of the flow and because of the rotation. It is thus important verify if existing numerical method for static wing can predict the ice accretion on rotor.

Student roles:
The trainee will participate in the simulation of ice accretion on rotor. The trainee must first get familiar with the relevant scientific literature. According to the skills and interests of the student, its tasks may include:
1. Build meshes on academic geometry relevant to icing problem;
2. Analyse data with visualisation software;
3. Do parametric studies;
4. Disseminate the results using scientific reports.

Skills required:
Good knowledge of viscous fluid mechanics (external flow) and heat transfer is essential. The ability to read and write technical reports in English is necessary. The trainee must have an interest in numerical simulation and programming.

Experiences with mesh generation tools, Computational Fluid Dynamics (CFD), flow visualization software are required and super-computing tools will be helpful.

Research area: Spacecraft constellations offer novel spacecraft missions that cannot be undertaken otherwise. There are two specific mission scenarios that are of interest here. The first is small constellations of formation-flying astronomy satellites to image extrasolar planets. In particular, terrrestrial-sized planets require interferometric techniques which in turn require submicron relative positioning of such spacecraft. The second mission is to control large swarms of nanosatellites at the Sun-Earth L1 lagrangian point to provide a solar shield.

Student roles:
The student will perform computer modelling of (i) or (ii) using Matlab/STK tools

Skills required:
The student will require an interest in orbit mechanics, control systems and robotics

Research area: Dr. Alex Ramirez-Serrano investigates and develops autonomous/intelligent reconfigurable ground and aerial unmanned vehicle systems for GPS-denied spaces, with applications to search & rescue and diverse industrial applications. Dr. Ramirez-Serrano performs R&D on control methods and algorithms for high-speed robot navigation in complex confined spaces, as well as on high-definition robot sensing/perception techniques. Research activities involve mechanical design, hardware, electronic design, and navigation/ control mechanisms. Software systems are often developed to enhance robot-user communication for optimal robot use. Dr. Ramirez-Serrano collaborates with diverse organizations and companies to find useful robot solutions.

Student roles:
The students working on this project will have to perform diverse tasks such as:
i) Design control mechanisms.
ii) Perform the needed simulation studies using available MATLAB software tools.
iii) Perform motion analysis of the robotic system.
iv) Develop and simulate control algorithms in Matlab and implement such control systems in a working prototype.
Students will work with software and hardware to create a working prototype of an underwater autonomous vehicle.

Skills required:
The student(s) participating in this project will be required to have knowledge and abilities to design and program effective control systems, have MATLAB knowledge/experience, and be interested in working with electrical robotic systems applied to underwater systems.

Research area: Dr. Alex Ramirez-Serrano investigates and develops autonomous/intelligent reconfigurable ground and aerial unmanned vehicle systems for GPS-denied spaces, with applications to search & rescue and diverse industrial applications. Dr. Ramirez-Serrano performs R&D on control methods and algorithms for high-speed robot navigation in complex confined spaces, as well as on high-definition robot sensing/perception techniques. Research activities involve mechanical design, hardware, electronic design, and navigation/ control mechanisms. Software systems are often developed to enhance robot-user communication for optimal robot use. Dr. Ramirez-Serrano collaborates with diverse organizations and companies to find useful robot solutions.

Student roles:
The students working on this project will have to perform diverse tasks such as:
i) Design control mechanisms.
ii) Perform the needed simulation studies using available MATLAB software tools.
iii) Perform motion analysis of the robotic system.
iv) Develop and simulate control algorithms in Matlab and implement such control systems in a working prototype.
Students will work with software and hardware to create a working prototype of an underwater autonomous vehicle.

Skills required:
The student(s) participating in this project will be required to have knowledge and abilities to design and program effective control systems, have MATLAB knowledge/experience, and be interested in working with electrical robotic systems applied to underwater systems.

Research area: Ornithopters are flying vehicles that generate lift and propulsion by flapping their wings, similar to birds, bats, or insects. The research examines how composite material design, such as fibre orientation, can be used to control wing bending, twisting, stiffness, and overall deformation behaviour.

Student roles:
As a Mitacs Globalink Summer Researcher, the student will contribute to the modelling, analysis, and evaluation of composite ornithopter wing structures.

Responsibilities include:
- Conducting literature reviews on composite wings, ornithopters, and fibre-reinforced materials.
- Developing simplified composite wing models for deformation analysis.
- Defining fibre layout configurations, loading conditions, and boundary conditions.
- Performing modelling or simulation to evaluate bending, twist, stiffness, and stress distribution.
- Comparing different fibre orientations against established performance criteria.
- Interpreting results and identifying design trends for lightweight bioinspired wing structures.
- Preparing technical reports and presentations summarizing project outcomes.

Skills required:
This project is suitable for students pursuing a degree in Mechanical Engineering, Materials Engineering, Aerospace Engineering, or a related discipline. Preference will be given to students in the later stages of their degree.

Required qualifications:
- Strong interest in composite materials, lightweight structures, and bioinspired aerospace systems.
- Ability to work independently and systematically.
- Excellent attention to detail.
- Strong written and verbal communication skills in English.

Assets:
- Experience with CAD or finite element modelling software.
- Understanding of classical laminate theory.
- Experience with numerical modelling and and experimental testing

Research area: We are developing the Digital Twin of a hydro unit, which will combine live sensor data with physics-based modeling through artificial intelligence to achieve real-time simulation. It will allow predicting failures, optimizing maintenance schedules, and simulate scenarios of usage and wear of the equipment.

Student roles:
You will join a team to work on the modeling or the experimental side of the project according to your strength and interests. Your tasks will vary between deriving equations, coding models, training neural networks, running simulations or designing, sizing, manufacturing, assembling and testing an experimental setup.

Skills required:
Skills are optional, motivation and will to learn are mandatory!
Modeling: coding (python, matlab, C), vibration and dynamics, finite element analysis, neural networks, reduced order modeling.
Experimenting: CAD (Catia, Solidworks), designing, machining, intrumentation.

Research area: Dr. Reda Snaiki is an Assistant Professor in the Department of Construction Engineering at ÉTS Montréal, Université du Québec. His research interests span wind engineering, structural engineering, coastal engineering, climate change, and artificial intelligence. He aims to develop advanced tools for assessing risks associated with extreme winds, in order to design more resilient infrastructure for future climate events.

Student roles:
The student will be actively involved in the hands-on experimental work within the new multi-fan wind tunnel facility. This role is primarily focused on gaining practical experimental skills and contributing to the research objectives. Key responsibilities will include:

Learning and Adhering to Procedures: Understanding and strictly following laboratory safety protocols, standard operating procedures for the wind tunnel, and instructions for specific experimental tasks.
Assisting with Experiment Setup: Participating in the preparation phase, which may involve constructing or modifying scaled building models, installing pressure taps or other sensors, mounting models in the wind tunnel test section, and assisting with the setup of data acquisition equipment.
Participating in Wind Tunnel Operation: Assisting qualified personnel in operating the multi-fan wind tunnel controls to achieve the desired wind conditions for each test run, according to the experimental plan.
Conducting Data Acquisition: Learning to operate the data acquisition systems (software and hardware) to record measurements from pressure sensors, force balances, or other instruments during the experiments.
Handling Experimental Data: Assisting in organizing, labeling, and managing the raw data files collected during testing. Performing initial data processing steps as instructed, which might include data format conversion, applying calibration factors, or calculating basic statistical quantities.
Contributing to Analysis: Assisting in the analysis of processed data under guidance, such as generating plots of pressure distributions, calculating aerodynamic forces, or comparing results across different test conditions using tools like MATLAB, Python, or Excel.
Supporting Flow Visualization: If part of the experiment, assisting in setting up, running, and documenting flow visualization techniques.
Contributing to summary reports or presentations.
General Lab Support: Assisting with maintaining a clean and organized laboratory workspace.

Skills required:
A basic understanding of fundamental fluid mechanics (pressure, velocity, forces) is required. Strong hands-on aptitude for laboratory work (experimental setup, equipment handling) is essential. Good data handling and analysis skills are necessary (tools like MATLAB, Python, or Excel beneficial for processing measurements). Attention to detail and good problem-solving abilities are important for experimental work. Enthusiasm for learning experimental methods, wind tunnel operation, and sensor/data acquisition technology is crucial. Experience with CAD software or physical model building would be advantageous but not strictly required.

Research area: My research field focuses on aerodynamics and fluid mechanics. The objective is to understand turbulence phenomena and boundary layer separation at the blade scale to improve energy efficiency. We use standardized airflow test benches to measure overall performance (pressure, flow rate, power) while integrating advanced measurement techniques. The challenge is to reconcile high air transfer performance with a drastic reduction in noise pollution, thereby meeting current environmental standards.

Student roles:
The student will be the operational lead for the experimental phase of the project. Their role spans the entire experimental loop development and data analysis process:

Test Bench Assembly & Instrumentation: The student will finalize the physical setup of the cooling loop. This includes mounting the thermal load simulators, integrating fluidic connections (pumps/fans, piping), and strategically positioning sensors (thermocouples, RTDs, differential pressure transducers, and flow meters).

Experimental Testing & Calibration: They will define test protocols, calibrate the sensors, manage measurement uncertainties, and conduct rigorous experimental runs under varying heat loads and flow rates to map the system's performance.

Data Processing: Using MATLAB or Python, the student will process raw thermal and hydraulic data to compute key performance indicators, such as the Nusselt number, heat transfer coefficients, and pressure drop penalties.

Reporting and Synthesis: The intern will document findings in technical reports, present results during lab meetings, and outline design recommendations for system optimization based on the trade-off between thermal efficiency and hydraulic losses.

Skills required:
A strong foundation in heat transfer (convection, conduction) and fluid mechanics is essential. Hands-on experience with laboratory instrumentation, including data acquisition systems (LabVIEW, Python, or Arduino), thermocouples, and flow meters, is highly required. Basic knowledge of CAD software (SolidWorks/CATIA) for component design and 3D printing is a major asset. The student must demonstrate strong practical problem-solving skills and autonomy in a laboratory environment.

Research area: The study of fluid-structure interaction exists in many disciplines such as biomechanics, medicine, and aerospace engineering. The classical theory of elasticity assumes small deformation in structures. However, the current trend in the aviation industry is to design more efficient aircraft with less drag, which leads to higher aspect ratio wing design and as a result, tighter aeroelastic coupling and larger deformations. Therefore, it is essential to find a viable model to accurately describe this large deformation, nonlinear aeroelastic coupling, and prevent disastrous incidents such as famous Tacoma bridge collapse or in-flight structural failure.

Student roles:
You will design, machine and build experimental setups, perform wind tunnel tests and perform simulations on a coupled CFD-FEM in-house code.

Skills required:
Good knowledge fluid mechanics, vibrations and numerical techniques of resolution of differential equations
Prior experience of coding (ideally in C and/or Python)
Experience in computational fluid dynamics and wind tunnel tests is an asset.

Research area: Wells injecting or collecting fluid are large scale engineering applications. The fluid flows through a heterogeneous porous matrix and enters the well pipe through a series of apertures. In the well the flow is unobstructed, but mixing occurs with all subsequent inflowing streams. Surprisingly little is known about the mathematics of macroscopic flow phenomena through a heterogeneous matrix, or the physics of the coupling between the two flow fields, resulting in control issues and moot success of the operation. I study the aspects of fluid dynamics of these systems. Past, active and open projects can be found at https://faculty.tru.ca/ynec/index_work.html .

Student roles:
The students will
(a) get acquainted with the existing flow models and code
(b) read literature on special functions as needed
(c) develop the mathematical model
(d) learn simulation software as needed (FlexPDE, Octave/MATLAB, VU)
(e) write code for graphical visualisation of the solutions
(f) compare the results of existing and new implementations
(g) run sensitivity tests
(h) write a report.

Skills required:
Skills / background:
(a) fluid dynamics
(b) concepts in numerical analysis
(c) concepts in dynamical systems, including ordinary and partial
differential equations
(d) basic programming, MATLAB/Octave an advantage

Background: engineering (mechanical/aeronautical),
physics, mathematics or combination of these disciplines.

Research area: The main research topic of our group is the study and characterization of complex unsteady flow phenomena with multiphysics couplings, including aeroacoustic, and aeroelastic phenomena. Such phenomena are present at multiple scales, from large, high-speed rotating machines (high-speed turbomachines and low-speed fans) to small biological flows (flexible cilia in the brain ventricles and choroid plexus). Understanding these phenomena is needed to help design more efficient, quieter, and safer systems. The lab (established in 2025) focuses on modelling such phenomena using (1) high-fidelity CFD simulations, (2) low-order analytical models, and (3) AI data-driven surrogates taking advantage of the other two approaches.

Student roles:
The intern will take ownership of the day-to-day development and analysis of the simulation, working under regular supervision. The role combines scientific modelling, scientific computing, and fluid-dynamics analysis.

Concretely, the student will:
(1) Become familiar with the fundamentals of the Lattice-Boltzmann Method and with the Palabos library, starting from existing example cases;
(2) Set up a 2D fluid domain representing a section of a ventricular cavity and implement a single immersed cilium with prescribed beating kinematics;
(3) Verify the numerical setup through appropriate checks — lattice resolution, physical-to-lattice unit conversion, and correct low-Reynolds-number behaviour against a reference case;
(4) Perform the numerical simulation, including a grid-convergence study
(5) Extract and analyse the relevant flow quantities, including velocity fields, streamlines, and the net transport induced by the cilium; and
(6) Generalise the model to multiple cilia with imposed metachronal phase shifts and compare pumping performance across beating patterns.
Throughout, the student will document the methodology, maintain clean and version-controlled code, and present progress in regular meetings, culminating in a written report and an oral presentation.

Skills required:
The student must have basic skills in fluid mechanics, structures, mathematics, and coding. Knowledge of Fluid-Structure Interaction coupling methods and C++ / Fortran is a plus. He/She should be able to read scientific publications to validate the CFD simulations. The simulations will be done using the Lattice-Boltzmann open-source solver Palabos (https://palabos.unige.ch/), written in C++.

Research area: Kirigami is the Janapese art of paper cutting. In this project, we leverage it to create parachutes out of thin sheets of polymer. With the proper cut pattern, the polymer sheet will deform to slow and stabilise the fall of the payload it supports. This study at the triple interface of aerodynamic, solid mechanics, and maker science seeks to develop new ways to make parachutes cheaply for humanitarian air drops.

Student roles:
You will exert your creativity to design new kirigami parachutes. Think of ways to make different cut patterns to achieve the desired behaviour.
You will then fabricate these parachutes with a laser cutter, 3D printers, and different hand tools.
You will perform wind tunnel tests as well as drop tests with the parachutes to observe and measure they dynamics. In the past we also performed drone drop tests.
Based on your capabilities and interests, you will perform finite element simulations.
You will be part of the Laboratory for Multiscale Mechanics (LM2) at Polytechnique where you will join a group of roughly 50 graduate students and interns. LM2 members come from all parts of the world. You will be part of a team, with whom you will have lunch and play volleyball (or some other activity) over the lunch breaks! At the end of your internship, you will write a report and present your work in a conference style talk in front of the LM2 members to get feedback.
Former MTACS Globalink interns with our group have gone on to grad school at MIT, Stanford, UIUC, Supaero, TU Delft, and even Polytechnique Montreal!
If the conditions are right, you might take part in writing a joint scientific publication.

Skills required:
Good knowledge fluid mechanics and solid mechanics.
At ease working with your hands to make and fabricate things. Willing to learn how to use a laser cutter, 3D printers and other fabrication tools.
Prior experience of coding (ideally in Python and/or Matlab).
Knowledge of the finite element method would be an asset.
Experience in computational fluid dynamics and wind tunnel tests is an asset.

Research area: Dr. Vijayaraghavan works on integrated physical and control system design optimization for renewable and alternate energy systems. He is currently focused on optimizing vertical axis wind turbines and fault diagnostics in fuel-cells

Student roles:
Role A or B or both
Role A:
An intern with a stronger background in programming would aim to improve the pervious simplified model using MATLAB simulation of fuel cells with various faults.
The intern can also focus on developing machine learning to distinguish between faults.
Role B:
An intern with more CFD background aim to conduct CFD simulations (in collaboration with graduate students working on this project).

Skills required:
The students require a strong engineering background. Background in Matlab, machine learning, Python, C++ would be an added asset. Experience in openfoam (or other CFD software) particularly in the area of fuel cells will also be valued.

Research area: My research interests are mainly related to additive manufacturing (AM) of multifunctional polymer composites. Past contributions include the development of innovative AM processes (e.g., 6-axis non-planar, multi-material multi-process, UV-assisted direct ink writing), and the design and fabrication of multifunctional materials (e.g., carbon fiber-reinforced thermoplastics, abradable thermosets, piezoelectric materials) for advanced applications mainly for aerospace but also with high potentials for transportation, energy and biomedical sectors.

Student roles:
- Help the team with printing of test specimens for mechanical and microstructural characterization
- help the team with conducting mechanical and microstructural testing
- Analyze results and report to the team
- Participate in various meetings (e.g., individual, group, and with the industrial partners)
- Write reports and present the work to the team

Skills required:
- Hands-on attitude
- Basic knowledge in additive manufacturing or 3D printing
- Basic knowledge in thermoplastics
- Team-working skills
- Basic knowledge in programming (e.g., Python) and software (e.g., CATIA)

Research area: My research focuses on advanced polymer composites and additive manufacturing for aerospace applications. I specialize in 3D printing of thermoplastic and thermoset composites, with emphasis on carbon fiber reinforced materials, non-planar toolpath optimization, and mechanical performance characterization. Current projects include self-healing polymers, recycled plastic optimization using machine learning, and impact resistance testing of composite structures. My lab combines experimental materials science with computational modeling to develop next-generation manufacturing processes for lightweight, high-performance aerospace components.

Student roles:
The student will actively participate in CFRTP drone frame development over the 12-week internship:

**Weeks 1-3: Training & Process Development**
- Learn composite materials fundamentals: CFRTP vs. thermoset, fiber orientations, layup techniques
- Study compression molding process: tool design, temperature/pressure/time parameters
- Design simple mold tooling for drone frame components (aluminum or steel plates)
- Source CFRTP prepreg materials (carbon fiber/PPS or PEEK from suppliers like Solvay, Sabic)
- Calibrate compression molding press: optimize cycle parameters for defect-free parts

**Weeks 4-7: Fabrication & Impact Testing**
- Fabricate 20-30 CFRTP specimens with varying parameters: fiber orientation (0°, 45°, 90°), stacking sequences ([0/90]s, [±45]s), thicknesses (2-6mm)
- Conduct drop-weight impact tests using ÉTS impact tower (ASTM D7136 protocol)
- Vary impact energies: 5J, 10J, 15J, 20J to map damage progression
- Document damage morphology: ultrasonic C-scan, visual inspection, cross-sections
- Measure peak force, energy absorption, permanent deformation for each specimen

**Weeks 8-10: Data Analysis & Comparison**
- Compile impact database: energy absorption vs. fiber orientation, thickness, stacking sequence
- Compare CFRTP performance against thermoset baselines (literature data or existing lab specimens)
- Identify optimal CFRTP configurations for maximum impact resistance
- Calculate cost-benefit: material costs, cycle times, performance gains vs. traditional composites
- Analyze failure modes: delamination, fiber breakage, matrix cracking patterns

**Weeks 11-12: Reporting & Knowledge Transfer**
- Create design guidelines: recommended CFRTP materials, layup sequences, molding parameters
- Prepare final report with manufacturing protocols and impact performance data
- Present results to lab group and discuss industry adoption pathways
- Document process parameters for future students
- Co-author conference abstract if results are significant (SAMPE, RAPID)

**Mentorship & Development:**
- Weekly 1-on-1 meetings with Prof. Tabiai for guidance and feedback
- Training on compression molding equipment and impact testing tower
- Access to graduate students (PhD/Master's) for peer learning on

Skills required:
Strong background in mechanical engineering, aerospace engineering, or materials science. Coursework in composite materials, polymer science, or mechanical testing preferred. Understanding of manufacturing processes (molding, forming, or additive manufacturing). Interest in aerospace applications and drone technology. Basic data analysis skills (Excel, Python, or MATLAB). Hands-on experimental mindset with attention to detail and safety protocols. No prior composite manufacturing experience required - full training provided on compression molding and impact testing. English proficiency required; French is an asset but not mandatory.

Research area: My research focuses on the physics of detonation waves and their application to advanced propulsion systems such as rotating detonation engines (RDEs). Detonations are supersonic combustion waves that release energy extremely rapidly, offering the potential for more efficient and compact propulsion than conventional deflagration-based systems. My group studies how fuel composition, droplet evaporation, and chemical kinetics influence detonation structure, stability, and limits. By improving our understanding of how detonations propagate in practical fuels, this research supports the development of next-generation aerospace propulsion systems for high-speed flight and space applications.

Student roles:
The student will play an active role in all stages of the research project. Their primary responsibilities will include designing and assembling experimental components and modifications to the existing apparatus, including gas and liquid handling systems for generating and characterizing monodispersed fuel droplets. The student will conduct detonation experiments, operate advanced diagnostics, and analyze the resulting data to identify trends in detonation structure and performance. They will work closely with graduate students and other members of the research group, contributing to experiment planning, troubleshooting, and interpretation of results. Through this hands-on involvement, the student will gain valuable experience in experimental design, data analysis, and advanced combustion research.

Skills required:
The ideal student will be enthusiastic, curious, and willing to learn new experimental and analytical techniques. A background in mechanical or aerospace engineering, combustion, thermodynamics, or fluid mechanics is desirable. Previous experience with laboratory work involving gas or liquid handling systems would be an asset, but is not required. Similarly, prior exposure to combustion or propulsion concepts is beneficial but not necessary. The most important qualities are strong problem-solving skills, attention to detail, and a motivation to contribute to cutting-edge research in advanced propulsion.

Research area: We design, fabricate, and develop novel sensors based on micro and nano-electromechanical systems (MEMS & NEMS) technology and thin-film quantum emitters for gas, chemical, and biological applications. The intern will join an active research group encompassing undergraduate and graduaet students. The group is equipped with three micro-motion laser vibrometers, multiple lock-in amplifiers, a VNA, a probe station, a nano-fabrication system, and a full complement of electrical drive and measurement instrumentation. He/she will participate in the design, fabircation, test, and analysis of MEMS, NEMS, and quantum gas, chemical, and biological sensors.

Student roles:
Under my supervision and in collaboration with of a graduate student
- Carry out experiments on MEMS, NEMS, and quatum sensors
- Design and test of drive and detection PCBs
- Design and implement drive and control codes for micro and nano positioners
- Functionalize sensors using the nano-fabrication system

Skills required:
- Design, handling, and post-processing of MEMS or NEMS
- Design, debug, and test derive and detection circuits and the corresponding PCBs
- Micro-scale experimental (fabrication and / or characterization) techniques
- Hands-on experience in the dynamics, vibrations, and / or control of electromechanical systems.

Research area: The main research topic is the noise generation and mitigation of propulsion and ventilation systems to address two major environmental issues that can cause serious health hazards. On the one hand, airplane noise yields increasingly sensitive issues to community near airports at approach and take-off conditions as air traffic increases. On the other hand ventilators in daily appliances, in transport systems, and in buildings or tunnels are exposing workers daily to noise levels likely to create hearing issues. Moreover, clean renewable energies such as farms of wind turbines can be more generally accepted if their noise is significantly reduced.

Student roles:
The student will have to first use the aeroacoustic tools developped in the research group, which will be the basis of his developments. He will initially collect information on the experimental campaign made at Virginia Tech [Alexander, Devenport] from the recent published papers. Initial aeroacoustic predictions with the available tool will highlight the contribution of the student. A bibliographic study will help the student to select and derive the appropriate model to model the turbulence interaction mechanism. He will then implement the model and validate it against the published daatbase. He will have to regularly report his progress to the research group, and write a final report. All along his internship he will be helped by the researchers and the students from the group (20 people).

Skills required:
The student must have competences in fluid mechanics, turbulence, acoustics, mathematics and coding. He should be able to read scientific publications, to derive analytical models. The coding will be done in matlab or equivalent (Python for instance).

Research area: I am working in the area of applied analysis with emphasis on partial differential equations in engineering and environmental/natural applications. Many projects are inter-disciplinary, involving theoretical concepts of mathematics, fluid mechanics, thermodynamics and control theory, but also numerical solutions to problems intractable analytically and optimisation problems. I partner with local industry, engineers, consultants and other faculty to share field data and provide insight into problems encountered in different disciplines. The research is oriented at improved engineering design or computation, educated decision making and deep understanding of the physics of the systems at hand, especially ones combining natural and man-made components.

Student roles:
The students will
(a) get acquatnted with the CAROLAB software
(https://www.creatis.insa-lyon.fr/carolab/)
(b) examine provided ultasound data and/or generate new data from existing
recordings
(c) get acquainted with the code in its current state (MATLAB/Octave)
(d) get acquainted with the current state of research by reading scientific
papers
(e) work on code and signal analysis with the aim to verify the
conservation of momentum in the tissue as a continuum; identify decoupled
movement and deformation components; calculate stresses experienced by the
wall separately due to heart forces and blood flow
(f) juxtapose results to the magnitude of related quantities reported in
the literature
(g) based on the above, build a model of haemodynamic-viscoelastic
interaction between the artery wall and blood flow
(h) write a report.

Skills required:
Skills / background:
(a) fluid dynamics and continuum mechanics: equations of conservation of
momentum in viscous flows and elastic media
(b) concepts in signal analysis: Fourier transform, smoothing techniques
(c) MATLAB/Octave an advantage

Background: engineering (mechanical/aeronautical), physics, mathematics or
combination of these disciplines.

Research area: Resilient Positioning, Navigation and Timing (PNT) for aviation, with a focus on GNSS security, spoofing detection, multi-constellation receiver resilience, sensor fusion, and artificial intelligence methods for robust navigation in safety-critical environments.

Student roles:
The student will support the development and validation of resilient GNSS technologies for aviation applications. The main role will be to contribute to the design, implementation, and testing of algorithms for GNSS spoofing detection, mitigation, and receiver resilience.

The student may participate in the analysis of GNSS and multi-constellation signals, the integration of IMU data, and the development of artificial intelligence or machine learning methods for anomaly detection. The work may also include simulation, data processing, performance evaluation, and comparison of different detection or mitigation strategies under nominal and spoofed conditions.

Depending on the student’s background, the role may involve programming in Python, MATLAB, or C/C++, working with experimental datasets, preparing technical documentation, and contributing to laboratory demonstrations or validation activities. The student will work under the supervision of researchers at the LASSENA laboratory and may interact with industrial partners involved in the project.

The expected contribution is both technical and analytical: the student will help transform research concepts into validated methods that can improve the safety, robustness, and reliability of future aviation navigation systems.

Skills required:
The ideal candidate should have a background in electrical engineering, computer engineering, aerospace engineering, software engineering, or a related field. Knowledge of GNSS, signal processing, navigation systems, sensor fusion, or radio-frequency systems would be an asset. Experience with Python, MATLAB, C/C++, data analysis, machine learning, or simulation tools is also desirable.

The student should be motivated, autonomous, detail-oriented, and comfortable working in a multidisciplinary research environment. Good analytical skills, technical writing abilities, and an interest in aviation safety, resilient navigation, and applied artificial intelligence are strongly encouraged.

Research area: The DREAM Lab (https://golovin.mie.utoronto.ca) develops novel materials through surface modification, towards the betterment of society. In many aspects, this means creating coatings with unique properties that can positively impact an engineering application. Some of the general themes within the lab include de-icing and anti-icing materials, low adhesion coatings, liquid repellent surfaces, and smart materials that combine sensors within the coating itself. The lab is about 90% experimental and uses a wide range of chemical, mechanical, and electrical techniques to fabricate, characterize, and analyze our new materials.

Student roles:
The intern will either have a defined subproject within a larger theme of the research group, or their own tangential project that relates closely to a current larger project of the group. The intern will be paired alongside either a PhD student or postdoctoral scholar, who will provide daily mentorship and guidance throughout the length of the intern's project. Likely the intern will be made responsible for creating data / figures for an academic publication, and success in this area will mean co-authorship on that work.

Skills required:
Students should possess a strong work ethic and be a very good team player. The DREAM Lab has 15 - 20 members, all working close to (both physically and academically) one another, and the intern should be able to work well with others accordingly. The projects are experimental so hands-on skills are a must, and any chemistry skills are a bonus and will help with the coating synthesis and fabrication (though not required as other lab members can fabricate the coatings for the intern).

Research area: We design, fabricate, and develop novel scanning, diffractive, and adaptive micro-electromechanical systems (MEMS) mirrors. The intern will join an active research group encompassing undergraduate and graduate students. The group is equipped with three micro-motion laser vibrometers, a probe station, a nano-fabrication system, and a full complement of electrical drive and measurement instrumentation. He/she will participate in design, post-processing, test, and analysis of MEMS and NEMS actuators and their circuits.

Student roles:
Under my supervision and in collaboration with of a graduate student
- Design and analyze MEMS actuators
- Carry out experiments on MEMS actuators
- Design and test drive and detection PCBs

Skills required:
- Design, handling, and post-processing of MEMS mirrors
- Design, debug, and test of drive circuits and their PCB implementations
- Micro-scale experimental (fabrication and / or characterization) techniques
- Hands-on experience in the dynamics, vibrations, and / or control of electromechanical and optomechanical systems.

Research area: We design, fabricate, and develop novel micro and nano-electromechanical systems (MEMS & NEMS) resonators as well as optomechanical resonators that exploit light (photon) pressure as an interaction force between optical and acoustic fields. Our target is to create high-quality phononic frequency comb generators with high modulation indeces, long coherence times, and a tunable free spectral range. The intern will join an active research group encompassing undergraduate and graduate students. The group is equipped with three micro-motion laser vibrometers, a probe station, a nano-fabrication system, and a full complement of electrical drive and measurement instrumentation. He/she will participate in design, post-processing,

Student roles:
Under my supervision and in collaboration with of a graduate student
- Design and analyze MEMS & NEMS resonators
- Carry out experiments on MEMS & NEMS resonators
- Design and test drive and detection PCBs

Skills required:
- Design, handling, and post-processing of MEMS & NEMS
- Design, debug, and test of drive circuits and their PCB implementations
- Micro-scale experimental (fabrication and / or characterization) techniques
- Hands-on experience in the dynamics, vibrations, and / or control of electromechanical and optomechanical systems.

Research area: The Turbulence Research Laboratory at the University of Toronto investigates turbulent and separated flows using experimental, computational, and physics-informed machine learning methods. Current work includes high-fidelity CFD simulation of bluff-body flows, reconstruction of velocity and pressure fields from sparse numerical data, and the application of Physics-Informed Neural Networks (PINNs) to extract physically consistent flow information beyond the resolution of the underlying simulation. Application areas include bluff-body aerodynamics, flow control, and environmental fluid mechanics.

Student roles:
The two interns will collaborate on the PINN model development and validation components of the project, with responsibilities divided to ensure parallel progress. One intern will focus primarily on the CFD data pipeline: preprocessing and sub-sampling the RANS/LES velocity and pressure datasets to create sparse training inputs across the four cube orientations, establishing the ground-truth comparison database, and conducting quantitative validation (divergence error, reconstruction RMSE, energy spectra). The second intern will focus primarily on the PINN implementation: designing and training the neural network architecture (inputs: 2D spatial coordinates and optionally time; outputs: velocity components and pressure), tuning the multi-component loss function (data loss, Navier-Stokes residual, boundary condition penalty), and benchmarking PINN-reconstructed fields against conventional interpolation methods.
Both interns will contribute to the comparative flow visualization across cube angles — streamlines, vorticity contours, and pressure maps — and are expected to collaborate on a joint written summary of results suitable for inclusion in a conference or journal publication. Each intern will present their component of the work to the research group at the close of the internship.
Day-to-day supervision will be provided by the principal investigator and a senior graduate student in the Turbulence Research Laboratory. The two interns will attend weekly joint progress meetings and are expected to coordinate closely throughout the 12-week period.

Skills required:
The ideal candidate has a strong foundation in fluid mechanics and experience with machine learning or deep learning applied to scientific computing. Proficiency in Python is essential; familiarity with PyTorch, TensorFlow, or JAX is highly desirable. Exposure to physics-informed neural networks or computational fluid dynamics (RANS, LES, or finite-volume methods) is a significant asset. The student should be comfortable working with structured numerical datasets and visualizing two-dimensional flow fields. Graduate-level coursework in fluid mechanics, turbulence, or numerical methods is preferred.

Research area: Dr. Vijayaraghavan works on integrated physical and control system design optimization for renewable and alternate energy systems. He is currently focused on optimizing vertical axis wind turbines and fault diagnostics in fuel-cells

Student roles:
Students would conduct CFD simulations (in collaboration with graduate students working on this project) and wind-tunnel experiments on 3D printed blades for the VAWT.

Skills required:
The students require a strong engineering background. Background in CFD and experimental fluid dynamics (particularly in wind turbines) would be an added asset.

Research area: Our research investigates the predictive maintenance and inspection of 3D printed components using artificial intelligence and non-destructive evaluation (NDE) techniques. We combine 3D scanning, infrared thermography, and data analytics to detect defects and predict the lifespan of polymer and composite parts used in advanced manufacturing.

Student roles:
The student will support the experimental inspection and AI analysis of 3D printed polymer and composite parts. Tasks will include:
Operating 3D scanners and thermal imaging systems to capture part geometries and surface temperature data
Assisting in labeling data (e.g., regions with visible or detected defects)
Training and testing machine learning models for defect detection and failure prediction
Participating in weekly lab meetings and presenting experimental results
Contributing to a research poster, report, or publication depending on the progress

The intern will gain hands-on experience in non-destructive testing, additive manufacturing, and basic AI tools under the guidance of expert researchers. The internship provides exposure to interdisciplinary research bridging mechanical engineering, material science, and data-driven design.

Skills required:
The ideal candidate should have basic knowledge of 3D printing, materials science, or mechanical design. Experience in Python, image processing, or machine learning is an asset. Curiosity and a willingness to learn experimental and analytical tools are essential.

Research area: I study the multidisciplinary design optimization of unconventional aircraft using computational fluid dynamics and numerical optimization. I use a supercomputer to automate the design optimization process while considering aerodynamics, mass properties, propulsion, and flight mechanics, and I uncover novel design principles for the unconventional, complex, and highly integrated blended-wing-body and strut-brace-wing aircraft configurations.

Student roles:
Students will assist in the implementation of structural models (stress and aerostructural vibrations) and mission variables within an initializer conceptual-design code. Laminar-flow and aft-thruster boundary-layer-ingesting engine models can also be considered if time permits. Once tested, models will be transferred to the high-fidelity supercomputer code. The goal is to produce a good initial estimate followed by a refined optimal SBW aircraft design that considers multidisciplinary trade-offs and, potentially, future technologies. Coupled models of aircraft weight and balance, propulsion, and flight mechanics will be optimized in conjunction with high-fidelity aerodynamics models. Finally, key design principles will be identified and published in the scientific literature.

Skills required:
The student requires skills in mechanical engineering (especially fluid mechanics), engineering design, and scientific programming (mainly Matlab; any other languages used have a similar syntax).

Research area: My research focus is on scalable nanomanufacturing of polymer and inorganic composites, structures and surfaces for applications in automotive, aerospace, consumer goods, health and energy sectors. Specifically, we’re investigating the rapid and large-scale synthesis of 2D layered nanomaterials for energy related applications. Another project in our group focuses on automotive composites.

Student roles:
Depending on the comfort level the student will work under the supervision of a senior graduate student or directly under the PI’s supervision. The student will be expected to understand the project basics, including any necessary background readings. From there the student will be asked to identify the relevant knowledge gaps and formulate verifiable hypothesis that will be tested through the course of the project through achieving specific objectives. The student will be expected to design experiments and conduct the proper tests. These tests will involve sample synthesis, involving high pressure and temperature equipment, and also sample characterization, involving electron microscopy, X-ray Diffraction, UV and IR spectroscopy, atomic force microscopy and mechanical testing. Following these tests, the student will be analyzing the collected data and highlighting any relevant observable trends. The results of the testing and analysis will be disseminated as publications, and the student will be expected to contribute to their preparation. Furthermore, the student is expected to participate in weekly project update meetings as well as weekly team meetings. At least once during the course of this project, the student will be presenting a group seminar. Finally, the student is expected to write a final project report at the conclusion of this project.

Skills required:
Background (college/undergrad level)
a. Physics
b. Chemistry
c. Engineering
d. Some exposure to compressible gas dynamics (preferred)

Skills
a. Communications (Written/Verbal)
b. Teamwork
c. Analytical/Reasoning
d. Hands-on attitude
e. MS Office (Excel, Word, PPT)
f. Basic Matlab

Research area: The main research topic of our group is the study and characterization of complex unsteady flow phenomena with multiphysics couplings, including aeroacoustic, and aeroelastic phenomena. Such phenomena are present at multiple scales, from large, high-speed rotating machines (high-speed turbomachines and low-speed fans) to small biological flows (flexible cilia in the brain ventricles and choroid plexus). Understanding these phenomena is needed to help design more efficient, quieter, and safer systems. The lab (established in 2025) focuses on modelling such phenomena using (1) high-fidelity CFD simulations, (2) low-order analytical models, and (3) AI data-driven surrogates taking advantage of the other two approaches.

Student roles:
The intern will take ownership of the day-to-day development and analysis of the neural surrogate, working under regular supervision within the lab. The role combines scientific machine learning, scientific computing, and aeroacoustic analysis. Over twelve weeks, the work progresses from the isolated-airfoil baseline to the coupled cascade case. Concretely, the student will:

(1) Become familiar with the fundamentals of neural surrogate modelling, domain adaptation, and continual learning, and review the physical assumptions of Amiet's leading-edge model and Posson's cascade model, including the origin of its spurious resonances, starting from existing example cases and frameworks;
(2) Assemble a parametric training database from Amiet's model, spanning gust wavenumbers, frequencies, and flow conditions, and set up a reproducible data-generation and training pipeline for the isolated-airfoil regime, including the network architecture and loss formulation;
(3) Train and verify a baseline differentiable surrogate, defining accuracy metrics, performing hyperparameter tuning, checking predictions against the analytical reference, validating computed gradients, and benchmarking inference speed and memory cost against the original formulations;
(4) Extend the surrogate to the cascade regime through continual learning, implementing and comparing strategies such as Elastic Weight Consolidation and "building-block" approaches on the existing cascade database, while monitoring accuracy retained on the isolated case;
(5) Extract and analyse the relevant quantities, including response amplitudes and phases across the frequency range, the smoothness of the regularized resonances, and the degree of catastrophic forgetting on the isolated-airfoil configuration, supported by diagnostic visualizations of the response surfaces; and
(6) Generalise and assess the unified model across both configurations, quantifying the accuracy, speed-up, and robustness gained relative to the original analytical models, and identifying remaining limitations and directions for future work.

Throughout, the student will document the methodology, maintain clean and version-controlled code, and present progress in regular meetings, culminating in a written report and an oral presentation.

Skills required:
The ideal candidate is a student in aerospace engineering, mechanical engineering, applied mathematics, or a related field, with programming experience (Python preferred) and an interest in analytical modelling. Knowledge of aeroacoustics and machine learning helps, but a willingness to learn rigorously matters more.
He/She should be able to read scientific publications to implement existing ML models.

Research area: Many of today's environmental challenges come down to one basic question: how do water and air move? The answer lies in fluid dynamics. The way currents and winds carry particles and heat shapes problems such as how pollution spreads, how ice melts, and how air moves around the things we build. Yet these processes remain poorly understood. In the TEE Lab (Turbulence and Environmental Experiments) at the University of Ottawa, students tackle these questions through hands-on fluid dynamics experiments, using a combination of cameras and light sources to reveal the physics behind real-world problems and contribute to protecting our environment.

Student roles:
The student will design and build a dye visualisation system for the TEE Lab recirculating water tunnel to produce clean and reliable flow visualisations. Building on a previous design, the student will learn how the system’s components work together, improve the setup, and use CAD and 3D printing to design and fabricate custom parts.

Once the system is working, the student will demonstrate and validate it by visualising the flow behind a melting ice model in the tunnel, using cameras to record how the flow evolves as the ice melts. The student will also update the documentation so the system can be easily used by others in the future.

By the end of the internship, the student will gain hands-on skills in experiments and data analysis. They will develop an engineering mindset by learning how to build and improve tools to answer real scientific questions. The student will also become part of a collaborative research team working on the fluid dynamics of icebergs and environmental flows.

Skills required:
We are looking for a student with a basic understanding of fluid mechanics and an interest in hands-on experimental work. This project is ideal for a student who enjoys building and fixing things, as it involves assembling, troubleshooting and improving a flow visualization system. Some programming experience in MATLAB or Python and basic CAD skills are helpful, but no prior research experience is required, as training will be provided. We also value curiosity, patience, strong organizational skills, and a collaborative spirit, as you will share the lab with other researchers.

Research area: Our main research topic is the noise generation and reduction of high-speed turbomachines and low speed fans in order to solve major environmental nuisances causing serious health hazards. Among the low speed machines, we study wind turbines. Indeed, clean renewable energies such as farms of wind turbines are seen as a viable alternative to petroleum or nuclear energies. They are needed and already available. However, they can be more generally accepted if their noise is significantly reduced. Our research group (20 people) is the largest and most active aeroacoustic group in Canada and in the world.

Student roles:
The student will have to first use the aeroacoustic tools developped in the research group, which will be the basis of his developments. He will initially collect information on the experimental campaign made by Oerlemans and the simulations made by Cotté from the recent published papers. Initial aeroacoustic predictions with the available tool will highlight the contribution of the student. A bibliographic study will help the student to select and derive the appropriate model to model the turbulence interaction mechanism. He will then implement the model and validate it against the published daatbase. He will have to regularly report his progress to the research group, and write a final report. All along his internship he will be helped by the researchers and the students from the group (20 people).

Skills required:
The student must have basic skills in fluid mechanics, turbulence, acoustics, mathematics and coding. He should be able to read scientific publications, to derive analytical models. The coding will be done in Python, Matlab or equivalent.