Salience map research in computer vision has extensively examined where human observers look in images and videos during free viewing. Despite cognitive psychology recognizing the role of behavioral goals for over 50 years, integrating task dependence into quantitative models and large open datasets is a recent development. This project aims to continue our research program into integrating existing machine learning/AI models and eye-tracking datasets related to goal-directed vision (e.g., visual search) while providing tools for model testing and validation. A key focus is on multimodal AI, particularly language-vision integration.
Research area, student roles & skills
Research area: Eye-movements, scanpaths, salience, computer vision, AI
Student roles: The student will address chosen problems (according to interests, fit, and ability/background) in this area and will take the lead on all aspects of the project (with close mentorship)
Skills required: Computer vision, PyTorch, and/or Visual psychophysics/ human behavior
2. Computational Identification of Ion Channel Targets for Neuropathic Pain Treatment
Neuropathic pain arises from maladaptive changes in peripheral and central nervous system signaling following nerve injury, yet effective mechanism-based treatments remain limited. A key contributor to neuropathic pain is hyperexcitability of dorsal root ganglion (DRG) sensory neurons, which leads to abnormal sensory input and enhanced activation of spinal nociceptive circuits. Although ion channels are known to regulate neuronal excitability, it remains unclear which specific channel alterations are most responsible for pathological firing and represent the most effective therapeutic targets.
This project aims to identify ion channel mechanisms that can serve as potential therapeutic targets for neuropathic pain using computational modeling approaches. Building on an existing conductance-based model of DRG sensory neuron excitability, the project will systematically evaluate how different ion channel modifications influence neuronal firing behavior associated with neuropathic conditions.
A biophysically based single-compartment DRG neuron model will be implemented using the NEURON simulation environment, incorporating key voltage-gated sodium, potassium, and leak currents derived from established electrophysiological studies. Following baseline validation against published neuronal firing properties, the model will be used as a virtual platform to simulate neuropathic pain conditions.
Systematic parameter manipulations will then be performed to represent pharmacological or pathological restoration of ion channel function. These include reversal of channel kinetic changes, modulation of conductance levels, and adjustment of persistent current. Sensitivity analysis will be conducted to determine which ion channel parameters most strongly reduce hyperexcitability features such as lowered threshold, increased firing frequency, and spontaneous activity.
Research area, student roles & skills
Research area: My research focuses on understanding the cellular and molecular mechanisms underlying neuropathic pain through the integration of experimental neuroscience and computational modeling. Using electrophysiological, behavioral, and imaging approaches in animal models, I investigate how alterations in peripheral sensory neurons contribute to pain signaling and neuronal hyperexcitability. I also develop computational models of sensory neurons to test mechanistic hypotheses, predict the effects of ion channel alterations, and identify potential therapeutic targets. This integrated approach advances our understanding of chronic pain mechanisms and supports the development of mechanism-based strategies for neuropathic pain treatment.
Student roles: The student will work closely with the supervisor and research team while utilizing computational and analytical resources provided within the academic environment. The primary role of the student will be to develop, implement, and analyze a conductance-based computational model of dorsal root ganglion (DRG) sensory neurons to investigate ion channel mechanisms underlying neuropathic pain. The student will be responsible for building and maintaining neuron simulation scripts using the NEURON simulation environment (or equivalent Python-based tools). This includes implementing biophysically realistic membrane properties, voltage-gated sodium, potassium, and leak currents, and ensuring model behavior aligns with published electrophysiological data. The student will perform systematic parameter manipulations to simulate neuropathic pain conditions and potential therapeutic interventions targeting ion channel function. A key responsibility will be conducting sensitivity and comparative analyses to determine how specific ion channel modifications affect neuronal excitability, including measures such as firing threshold, spike frequency, action potential waveform, and spontaneous activity. The student will also interpret simulation results to identify which ion channel parameters most strongly regulate hyperexcitability and may therefore represent promising therapeutic targets. In addition to model development and analysis, the student will maintain clear documentation of all computational methods, simulation protocols, and results to ensure reproducibility. The student will participate in regular research meetings, contribute to discussions on model refinement, and integrate feedback into ongoing simulations. The student will prepare figures, data visualizations, and written summaries of findings, culminating in a final research report. They will also present results at academic venues such as the Redeemer Summer Research Symposium (August 2027). The expected outcomes of this work may contribute to a peer-reviewed publication, providing the student with valuable experience in computational neuroscience, data analysis, and scientific communication within a translational research context focused on neuropathic pain therapeutics.
Skills required: Successful candidates should have strong interest in neuroscience, computational modeling, and quantitative analysis. Preferred backgrounds include neuroscience, biology, biomedical engineering, computer science, physics, mathematics, or related fields. Basic programming experience in MATLAB or Python and familiarity with neurophysiology or electrophysiology concepts are assets, but not required. More important than prior technical expertise are strong analytical skills, attention to detail, and willingness to learn new computational and theoretical methods. The project provides training in conductance-based neuronal modeling using NEURON software and data-driven simulation approaches. Students with curiosity, persistence, and strong work ethic will be well suited for this interdisciplinary research project.
3. Computational Modeling of Ion Channel Dysregulation and Quantum-Inspired Effects on Pain Threshold in Sensory Neurons
Ion channels are essential regulators of neuronal excitability and play a key role in setting the resting membrane potential and action potential threshold in dorsal root ganglion (DRG) sensory neurons. In neuropathic pain, even subtle alterations in ion channel function can depolarize neurons, lower firing thresholds, increase spontaneous activity, and enhance responsiveness to peripheral stimuli. These changes contribute to abnormal sensory processing and pain hypersensitivity. While classical electrophysiological models describe ion channel gating through voltage-dependent conformational changes, emerging theoretical frameworks in quantum biology suggest that molecular-scale quantum effects may influence hydrogen-bond networks and protein dynamics within ion channels.
This project aims to investigate whether quantum-inspired modifications of ion channel gating can influence neuronal excitability and pain threshold regulation in sensory neurons. The focus will be on voltage-gated ion channels that stabilize membrane potential and control repetitive firing in DRG neurons.
A conductance-based computational model of a single-compartment DRG neuron will be developed using the NEURON simulation environment. The model will include potassium, sodium, and leak currents based on established electrophysiological data. Baseline neuronal behavior will be validated against published experimental measurements, including resting membrane potential, spike threshold, and firing frequency.
Quantum-inspired effects will be implemented as parameter-level modifications of potassium channel gating, including changes in activation kinetics, voltage dependence, and open probability. These represent hypothetical quantum-assisted perturbations potentially arising from proton tunneling or altered hydrogen-bond dynamics.
Systematic simulations will evaluate how these modifications affect neuronal excitability, including resting membrane potential, firing threshold, spike frequency, and stimulus responses. Sensitivity analysis will identify which ion channel parameters most strongly influence hyperexcitability.
Overall, this study will provide a computational framework linking ion channel function, neuronal excitability, and quantum-inspired mechanisms, generating testable predictions for neuropathic pain research.
Research area, student roles & skills
Research area: My research focuses on understanding how ion channel dynamics regulate neuronal excitability and pain signaling, with an emphasis on ion channels in sensory neurons. These channels are critical for maintaining resting membrane potential and controlling action potential threshold in dorsal root ganglion neurons. I use computational modeling approaches to study how alterations in ion channel gating influence neuronal firing and contribute to neuropathic pain. My work integrates neuroscience, biophysics, and mathematical modeling to explore both classical electrophysiological mechanisms and emerging theoretical concepts, aiming to generate mechanistic insights into pain regulation and identify potential ion channel targets for future therapeutic strategies.
Student roles: The student will play an active and interdisciplinary role in this research project, contributing to the development and computational investigation of ion channel mechanisms underlying neuronal excitability and pain threshold regulation in dorsal root ganglion (DRG) sensory neurons. The main objective is to study how ion channel gating dynamics regulate resting membrane potential, action potential threshold, and firing behavior, and how these processes may be influenced by quantum-inspired biophysical mechanisms. The student will begin by conducting a literature review in ion channel physiology, pain neuroscience, and computational neuroscience, with additional focus on foundational quantum theory concepts relevant to biological systems. This includes quantum tunneling and hydrogen-bond network dynamics, which have been proposed in quantum biology as potential contributors to conformational changes in ion channels. Next, the student will develop a conductance-based computational model of a DRG neuron using the NEURON simulation environment or equivalent tools. The model will incorporate key ion channel currents, including sodium, potassium, and leak currents, and will be validated against established electrophysiological properties such as resting membrane potential, firing threshold, and action potential characteristics. The student will then implement quantum-inspired modifications to ion channel gating parameters. These modifications will represent hypothetical quantum effects, such as tunneling-mediated changes in hydrogen-bond stability, which may alter channel activation kinetics, voltage dependence, or open probability. These effects will be introduced as parameter-level perturbations rather than explicit quantum mechanical simulations. The student will work with the supervisor, making full use of the resources and facilities offered by the academic environment. Student is expected to plan and conduct experiments; independently record, analyze, and interpret data; discuss research results in a research group, write research reports and present results at workshops, e.g. “Redeemer Summer Research Symposium”; provide recommendations at the conclusion of the project; results have potential to lead to a publication.
Skills required: Students should have a strong background in neuroscience, biophysics, physics, biomedical engineering, computer science, or related fields, with a basic understanding of neuronal function and ion channel physiology. An introductory knowledge of quantum mechanics, including concepts such as tunneling and basic quantum states, is required. Experience in mathematical modeling and programming (e.g., Python or MATLAB) is essential. Familiarity with computational neuroscience tools such as the NEURON simulation environment is an asset. The project requires strong analytical thinking, problem-solving skills, and an interest in quantitative approaches to studying ion channel mechanisms underlying neuropathic pain through computational modeling and simulation.
4. Deep brain stimulation to treat paralysis in a rat model
You will develop new methods of deep brain stimulation to improve locomotion and hand movements after paralysis, in the rat model.
We use deep brain electrodes to deliver stimulation of deep areas of the brain controlling movement execution and learning.
These technique can probe transmission of motor commands between the brain and the muscles.
This project will allow you to develop multiple cutting-edge neurotechnology skills:
- recording and decoding brain signals from behaving rats
- develop and deliver the most advanced brain stimulation interventions
- study hand kinematics with an unforeseen degree of precision, enabled by video AI processing
- discover and implement new neuroprosthetic and BCI technology.
What science should you expect? Check out some of our previous publications:
An intracortical neuroprosthesis immediately alleviates walking deficits and improves recovery of leg control after spinal cord injury | Science Translational Medicine
Brain-controlled modulation of spinal circuits improves recovery from spinal cord injury | Nature Communications
Research area, student roles & skills
Research area: The objective of the sciNeurotech Lab is to provide the cornerstone demonstrations for the next generation of intelligent neurostimulation technology for movement rehabilitation.
We develop new neurostimulation therapies, aiming at restoring sensorimotor function after neurotrauma, translationally from discovery in rodent to application in human medical technology, tailored and personalized to each user by artificial intelligence.
Student roles: She/he will: -train rats in behavioral tasks, which we use to evaluate hand and arm motor function and movement quality. - participate in all animal care required around implantations, behavior and neuromodulation delivery. - contribute establishing a pipeline of data analysis and use this pipeline to analyze movement in intact rats and rats with spinal cord injury. - perform behavioral recording aiming at identifying the effects of neuromodulation delivery in resting rats and in rats involved in motor tasks. Our objective is to identify the level of controllabiltiy to arm and hand motor function that we can obtain through brain neuromodulation. -perform analysis of electrophyisiology data, learn and apply state of the art techniques in processing brain and muscle signals. - perform behavioral recordings and analyses to evaluate the levels of spontaneous recovery of movement and the immediate and chronic imrovement of motor function obtained through neuromodulation delivery and neuroprosthetic training. - participate in journal clubs, lab meeting and social activities. - present her/his results at the end of the internship.
Skills required: This project is open to engineering students, as well as students in neuroscience or related disciplines.
Neuroscientists and related fields must come with experience in system neuroscience or rehabilitation. Coding is a strong asset.
Engineers are required to have strong Python or Matlab coding skills, experience in data science / ML / AI or in neurotechnology.
This project is in rodent models of neurotrauma. Handling animal models requires care, discipline and strong work ethics.
5. Dissecting brain-wide functional circuits underlying social decision making by developing a naturalistic virtual reality platform of juvenile zebrafish
Understanding the neural basis of social decision making poses a major challenge in systems neuroscience. This is because decision making during social interactions often occurs moment-by-moment and requires interactions from multiple separated brain areas. Previous studies have identified various brain regions and neural signatures, but also reveal two difficulties: 1) these brain regions are often studied in isolation due to technical barriers, thus hiding brain-wide communication, and 2) social interactions are highly dynamic while current behavioral paradigms are often static with pre-defined discrete task epochs, thus compromising ethological validity. Therefore, we aim to study how brain-wide neural dynamics support moment-by-moment social decision making in a naturalistic context.
To achieve this, we will overcome two technical barriers: (1) recording whole-brain neurodynamics at a high speed and cellular resolution, and (2) creating a naturalistic experience for animals by real-time sensory feedback during neural recording. We will develop a virtual reality platform enabling juvenile zebrafish to socialize with real-time feedback and combine it with whole-brain 2-photon calcium imaging.
We will use shoaling, the common social affiliation of fish species, and focus on social interactions mediated by visual cues that can be dynamically precisely controlled: Identify neural signatures of shoaling driven by biological motion (Aim 1) and fish-like appearance (Aim 2).
By combining these two technical innovations, our research will identify brain-wide neural representations of social behavior. As the underlying brain networks can be relatively evolutionarily conserved across vertebrates, the neural mechanisms we will discover using juvenile zebrafish will be transferable to mammals. Our results will produce a dynamic picture of brain networks in social interaction and provide basic knowledge for understanding neurological disorders with social deficits.
Research area, student roles & skills
Research area: Lin Lab features whole-brain neural recordings of behaving animals and quantitative and optogenetic tools to understand the neural mechanisms underlying cognition and behaviors. The central question of Lin Lab is to study how the brain produces adaptive, flexible behavior.
We take a multi-disciplinary and holistic (systems) approach by combining whole-brain neural imaging and computational tools on behaving animal models in virtual realities to study the neural mechanisms underlying cognition and behaviors at the systems level. To access the whole brain with single-cell resolution, we work with zebrafish and state-of-the-art microscopy. We develop data-driven computational models to predict behaviors from neurodynamics.
Student roles: The proposed research will use a virtual reality closed-loop system to study the social attraction evoked by biological motion in zebrafish. The virtual reality system will be designed to simulate a two-dimensional environment with natural scenes and moving objects, to trigger social attraction, as well as avoidance and hunting behaviors for comparison.
Improve the virtual reality system: Using the game engine Godot, we have developed a virtual reality system. The student will further tailor the VR for studying social attraction in zebrafish. This system consists of a custom-built arena that can be used to display visual stimuli and track behavior. The arena is equipped with cameras to monitor zebrafish behavior, which will be used to create closed-loop feedback and adjust the visual stimuli in real-time.
Characterize social attraction behavior in zebrafish: In the virtual reality system, students will use moving dots, or fish-like appearance, to trigger the shoaling behavior. Moving dots have different kinetics of conspecifics, predators, and paramecia, to measure the shoaling performance. We will also investigate the effect of different environmental conditions, such as lighting and temperature, on shoaling.
Explore the dependence of experience in social attraction: We will use the virtual reality system to explore the role of experience in social attraction behavior. We will use a variety of visual stimuli to simulate different social experiences and investigate how these experiences influence social attraction behavior.
To summarize, here are the tasks: 1. Optimize VR and behavioral devices for zebrafish using Python, Unity, or other tools 2. Perform behavioral assays and multi-photon neural imaging of zebrafish 3. Record, analyze, and model brain-wide neuronal time series
Skills required: 1) Mechanical/electrical/Optical engineering, physics, and CS students, with a strong passion/curiosity in neuroscience, are extremely welcome. 2) Quantitative skills, such as calculus, linear algebra, & statistics, are required. 3) Programming skills ( Matlab, Python, C, Java, …) are required; experience with Arduino / Raspberry Pi is a big plus. 4) Experience with animal behavioral assays is a plus. 5) Highly motivated and result-oriented.
6. Implementation of a EEG-based neurofeeback approach that uses brain stimulation to improve motor function
The project aims at designing a closed-loop non-invasive brain stimulation (NIBS) neurofeedback system based on real-time EEG monitoring of brain activity. The project involves designing the software to detect the EEG variable to monitor and modulate with the NIBS, identifying and adapting the controller to be used to trigger the NIBS, as well as writing scripts to synchronize all peripherals, including the force transducer, the EMG activity, and the visual display with the EEG system. This will allow the synchronization in time of the behavioural and the EEG data, as well as the triggering of the stimulation based on an EEG feature at the specific time around movement initiation. Ultimately, our goal is to test the newly developed closed-loop prototype as a proof-of-concept for its usability. The goal will be to compare its effects on the selected biomarker and motor performance variables to an open-loop protocol.
Research area, student roles & skills
Research area: Dr. Boudrias' main research focus aims to understand the neurophysiological mechanisms that underlie the interaction between brain areas in healthy and aging subjects, as well as in long COVID and stroke patients. She uses multimodal functional neuroimaging and electrophysiological techniques as well as state-of-the-art data analysis methods to measure the precise dynamics of brain interactions. The general aim of her research is to identify robust and sensitive biomarkers of motor ability. Her long term goal is to design subject-specific therapeutic interventions using physical training, music therapy or stimulation protocols to maintain motor function and/or alleviate age-related declines in motor performance.
Student roles: The student will participate in ongoing studies taking place in the laboratory. The student will be responsible to collect and analyse the preliminary data collected. The student will be required to read relevant recent scientific literature, and provide interpretation of the results in collaboration with Dr. Boudrias. The student will participate in the regular (weekly) meetings of Dr. Boudrias’ group and after completion of the internship he/she will present the results to the group as well as author a final technical report.
Skills required: Self-motivated student with a core background in neuroscience and programming skills. The student should be familiar with programming languages such as C++, Python or have prior experience working with MATLAB. Some knowledge of electrophysiology, signal and image processing is desirable. As the student will be working closely with a PhD student in Dr. Boudrias' group, being a good team player is important.
7. Multimodal Biomarkers of Attention, Memory, and Emotion in the Aging Brain
Healthy aging is associated with changes in cognitive and emotional processing, but the underlying neural and physiological mechanisms remain incompletely understood. This project will investigate how individuals across the adult lifespan process cognitive, memory, and emotional information using a combination of EEG, eye tracking, and physiological recordings.
Students will contribute to studies examining attention, memory performance, and emotional perception. Depending on their interests and research skills, they may work with existing datasets and/or participate in ongoing data collection. The project offers opportunities to analyze brain activity, eye movement patterns, and physiological responses during well-established experimental tasks.
The student will work with graduate trainees to gain hands-on experience in human neuroscience research, multimodal data analysis, experimental design, and scientific communication.
This project is suitable for students interested in neuroscience, psychology, biomedical engineering, and related fields.
Research area, student roles & skills
Research area: Our laboratory, CANLab, investigates cognitive and emotional brain function across the adult lifespan using multimodal neuroscience methods. We combine electroencephalography (EEG), eye tracking, and physiological measures such as heart rate, skin conductance, and body temperature to examine attention, memory, and emotion recognition. Our research aims to identify objective markers of brain health and understand how cognitive and emotional processes change with age. Findings from our work contribute to the early detection of cognitive decline and the development of strategies to support healthy aging and brain function.
Student roles: The student will assist with research activities that may include participant testing, data collection, data management, signal processing, statistical analysis, literature reviews, and knowledge dissemination. Depending on their background and interests, the student may also contribute to experimental design, interpretation of findings, and manuscript development.
Skills required: Applicants should have a background in neuroscience, psychology, biomedical engineering, computer science, or a related field. Experience with research methods, statistics, programming (e.g., R, Python, or MATLAB), EEG, eye tracking, or physiological signal analysis is preferred. Strong analytical thinking, attention to detail, and effective communication skills are important. Motivated students with an interest in brain function, cognition, emotion, and aging are encouraged to apply.
My research is organized around three complementary projects, all motivated by two major limitations of current deep learning: an excessive need for data and weak out-of-distribution generalization. I draw inspiration from the mammalian brain, which overcomes these obstacles with remarkable efficiency.
RL-Map Project. Reinforcement learning in the real world faces two difficulties: the amount of experience required and an inability to adapt to a changing environment. Inspired by the abstract maps the brain constructs, this project proposes adding a fourth module to the agent—a mapping module—that builds a dynamic map of the environment. By decoupling policy learning from the environment's current configuration, we hope for an agent that is more robust and less data-hungry.
Tensor Objects for Combinatorial Generalization Project. Rather than representing data as a vector, as is standard, this project proposes an object-based representation in which each object is the tensor product of its properties. Two hypotheses follow: this representation should improve sample efficiency through models that are powerful yet have fewer parameters, and it should enable combinatorial generalization—recognizing, for instance, a blue triangle after seeing only red triangles and blue circles.
Deep Local Unsupervised Learning Project. Deep learning rests on backpropagating a global objective function, whereas the brain learns through local rules such as Hebbian learning. These local rules generalize better and resist catastrophic forgetting, but remain ineffective in deep networks. This project aims to design local, unsupervised learning rules that learn effectively in a multilayer (deep learning) setting.
Research area, student roles & skills
Research area: My research develops more efficient and robust artificial intelligence, inspired by the mammalian brain. I tackle two major limitations of current deep learning: its enormous data requirements (poor sample efficiency) and its weak out-of-distribution generalization. Three directions structure this work: equipping reinforcement learning agents with a mapping module to navigate changing environments; representing data through objects—the tensor product of their properties—to enable combinatorial generalization; and designing local, unsupervised learning rules capable of deep learning, in the spirit of Hebbian learning.
Student roles: The student will take on a support role alongside graduate students, contributing concretely to the advancement of their research projects in machine learning. This is an assistantship designed to provide a solid first research experience while easing the technical workload of more experienced researchers.
Primary responsibilities include: implementing and debugging code in Python (notably with frameworks such as PyTorch or JAX); setting up, running, and monitoring model-training experiments; processing, cleaning, and visualizing datasets; and carefully documenting results and experimental protocols. The student may also be asked to carry out targeted literature reviews and to summarize relevant scientific papers.
The ideal candidate has a good foundation in Python programming and excellent mathematical skills (linear algebra, probability, calculus), along with intellectual curiosity, rigor, and independence. Prior knowledge of machine learning is an asset. Strong communication skills and the ability to collaborate within a team are essential.
In return, the student will benefit from close mentorship, an introduction to cutting-edge research methods, and a valuable opportunity to contribute to projects that may lead to publications.
Skills required: Advanced Linear Algebra Advanced Calculus Advanced Probability Theory Python Programming PyTorch Data visualization
9. Neurotechnology to recover hand function after a paralysis
You will develop implantable neuromodulation interventions to improve hand movements after paralysis, in the rat model.
We use implantable brain interfaces to deliver distributed stimulation of the brain movement control networks.
These technique can probe transmission of motor commands between the brain and the hand/arm muscles, with a dual advantage:
1) tracking emergent changes in the brain during motor recovery,
2) controlling and improving motor execution (reversing paralysis deficits!) via neurostimulation and brain-computer interfaces (BCI).
This project will allow you to develop multiple cutting-edge neurotechnology skills:
- recording and decoding brain signals from behaving rats
- develop and deliver the most advanced brain stimulation interventions
- study hand kinematics with an unforeseen degree of precision, enabled by video AI processing
- discover and implement new neuroprosthetic and BCI technology.
What science should you expect? Check out some of our previous publications:
An intracortical neuroprosthesis immediately alleviates walking deficits and improves recovery of leg control after spinal cord injury | Science Translational Medicine
Brain-controlled modulation of spinal circuits improves recovery from spinal cord injury | Nature Communications
Research area, student roles & skills
Research area: The objective of the sciNeurotech Lab is to provide the cornerstone demonstrations for the next generation of intelligent neurostimulation technology for movement rehabilitation.
We develop new neurostimulation therapies, aiming at restoring sensorimotor function after neurotrauma, translationally from discovery in rodent to application in human medical technology, tailored and personalized to each user by artificial intelligence.
Student roles: She/he will: -train rats in behavioral tasks, which we use to evaluate hand and arm motor function and movement quality. - participate in all animal care required around implantations, behavior and neuromodulation delivery. - contribute establishing a pipeline of data analysis and use this pipeline to analyze movement in intact rats and rats with spinal cord injury. - perform behavioral recording aiming at identifying the effects of neuromodulation delivery in resting rats and in rats involved in motor tasks. Our objective is to identify the level of controllabiltiy to arm and hand motor function that we can obtain through brain neuromodulation. -perform analysis of electrophyisiology data, learn and apply state of the art techniques in processing brain and muscle signals. - perform behavioral recordings and analyses to evaluate the levels of spontaneous recovery of movement and the immediate and chronic imrovement of motor function obtained through neuromodulation delivery and neuroprosthetic training. - participate in journal clubs, lab meeting and social activities. - present her/his results at the end of the internship.
Skills required: This project is open to engineering students, as well as students in neuroscience or related disciplines.
Neuroscientists and related fields must come with experience in system neuroscience or rehabilitation. Coding is a strong asset.
Engineers are required to have strong Python or Matlab coding skills, experience in data science / ML / AI or in neurotechnology.
This project is in rodent models of neurotrauma. Handling animal models requires care, discipline and strong work ethics.
10. Optimizing Myoelectric Prosthesis Control Training
Supervisor: Jacqueline Hebert
University: University of Alberta (Edmonton campus)
State-of-the-art prosthetic devices offer control over multiple degrees of freedom, with the potential to restore independence and improve quality of life for people with upper-limb loss. These devices are often controlled using advanced machine learning algorithms that interpret signals from the residual limb to infer the user’s intended movement. However, effective use of these systems requires training that is tailored to each user’s proficiency and skill level. As a result, researchers are increasingly focused on designing effective training environments that help people with limb loss learn to control prosthetic devices. This includes ongoing work in the Bionic Limbs for Improved Natural Control (BLINC) Lab at the University of Alberta.
We hypothesize that training is most effective when it is tailored to keep the user at an optimal cognitive load, thereby maximizing skill acquisition and motor learning. Prior research has shown that physiological signals such as heart rate variability, galvanic skin response, and pupillometry can be used to infer cognitive load and stress. However, these methods have not yet been fully tested in the context of prosthesis control. To address this gap, we are conducting experiements in the BLINC Lab to evaluate how reliably physiological sensors can detect and distinguish different levels of cognitive load during simulated prosthesis-control tasks in a virtual environment. The results will inform the design of future prosthesis training paradigms.
Research area, student roles & skills
Research area: My lab brings together a diverse group of inter-disciplinary researchers interested in collaborative research to improve sensory motor control and integration of advanced prosthetic and robotic systems. The lab encompasses research projects advancing prosthetic treatment options for persons with limb loss. We are intensely interested in the measurement of human systems behaviour that allows us to investigate the impacts of technological interventions on clinical outcomes. Our unique combination of medical, rehabilitation, engineering and computing science researchers has allowed the evolution of multiple lines of complementary research aimed at improving the science and art of prosthetic restoration and rehabilitation robotics.
Student roles: Help pilot and refine experimental protocols and virtual training tasks. Assist with conducting experiments and collecting participant data. Analyze physiological sensor data related to cognitive load and stress. Support research documentation, including summaries, reports, and presentations. Collaborate and contribute to a multidisciplinary research team environment.
Skills required: Background or strong interest in neuroscience, psychology, biomedical engineering, kinesiology, or a related field. Basic understanding of cognitive load, stress, attention, motor learning, or human performance. Interest in designing and conducting human-subject experiments. Strong collaborative and communication skills. Experience with virtual reality, or virtual task design is an asset, but not required.
11. Role of social norms and social hierarchy processing in behavior
My research interest gravitates around two extremely important social constructions: social norms and social hierarchy.
My main research aims at using a novel approach based on computational neuroscience to study the origin of individual differences in the processing of social norms and how some contexts can alter this processing.
I am also interested in improving our understanding on how our brain can: 1) establish social hierarchies; 2) adapt to changes in social hierarchies; and 3) use information related to social hierarchy in order to guide behavior.
Several stimulating projects in these research domains will be available to students.
Research area, student roles & skills
Research area: Our research team is built on a multidisciplinary framework—between neuroscience, social psychology and cognitive science—and uses innovative methods to tackle important concepts regulating our social interactions: social hierarchy and social norms. We propose a novel and exciting approach combining tools from Game theory, behavioral modeling, physiological measures and electroencephalography (EEG) to characterize processing of social hierarchy and social norms.
Student roles: Through this project, we aim at offering a high quality research experience. Importantly, we hope to show the student the importance and potential of trans-disciplinary approaches, where neuroscience, cognition and social psychology merge to open new and innovative ways to study social concepts.
The student will be under the supervision of Dr. Hétu. Dr. Hétu will provide high quality mentoring in experimental design, behavioral modeling and electrophysiological and physiological analysis giving the student the opportunity to develop a rare and cutting edge expertise. Thus, the student will be ideally positioned to learn how to harness the full potential of this tool in the study of social concepts.
The student will be working on every step of this project gaining valuable experience including the use of EEG an physiological material and acquiring programming skills that are increasingly important in research. More specifically, the student will help in data collection/organization and analysis (we hope to have the recruitment done before the start of the internship so the student will be able to spend most of his time analyzing data). By giving him important responsibilities we hope that he will gain transferable competences that will be very useful in graduate school or if he decides to pursue a career in organizations (governmental, NGO, etc.) where the capacity to use/produce scientific data is in high demand. We will also provide him with opportunities to present at a conference and/or submit the results in a peer-reviewed journal. This will help him build a competitive application for higher degree programs and scholarships—we are very open to support such applications should the student wish to pursue his training with us.
Skills required: The student should have background in psychology and/or neuroscience. Some knowledge in statistical analysis, EEG and programming would be an asset. The student will have the opportunity to work within a stimulating environment at the Université de Montréal under the supervision of Dr. Hétu. He will also have access to the dynamic environment of the CogNAC (the UQTR cognitive neuroscience research group) which offers scientific meetings, as well as workshops throughout the summer. The student is thus expected to contribute to the lab social life and scientific activities but will also benefit from the support, and knowledge of other students.
12. The role of NMDA receptors in astrocyte-neuron interactions
Supervisor: Jillian Stobart
University: University of Manitoba (Winnipeg campus)
Our research focusses on NMDA receptor signaling in astrocytes. We hypothesize that these receptors are activated by glutamate released from neurons and cause calcium events in astrocytes. We want to know: does astrocyte NMDA receptor signaling trigger gliotransmission as a way for astrocytes to influence nearby neurons? What type of neurons and synapses are involved in this astrocyte-neuron communication?
Research area, student roles & skills
Research area: Astrocytes are a type of brain glial cell that are in close contact with neurons. Recently the neuroscience field has become interested in how astrocytes have influence neuronal activity and possibly play a role in animal behaviour.
Student roles: Students who join our energetic team will have the opportunity to learn two-photon microscopy, the latest, state-of-the-art technique in neuroscience. Students will also learn how to work directly with mice, including, handling, training, and injections. Students will also gain valuable computer skills by learning to analyze movies of calcium fluctuations or electrophysiology recordings with programs such as MATLAB and R. Finally, students will also develop communication and problem-solving skills by participating in regular lab meetings in a group setting.
Skills required: Knowledge of biology is essential. Previous research experience in a lab setting is beneficial, particularly with research animals. Understanding of computer programming (for data analysis) and statistics is a plus.
13. The role of brain pericytes in the neurovascular unit
Supervisor: Jillian Stobart
University: University of Manitoba (Winnipeg campus)
Pericytes are cells found on brain capillaries. Exciting new evidence suggests that pericytes may regulate the blood-brain-barrier and dilate capillaries to increase blood flow where needed. Both of these roles are essential for brain health and pericytes may become dysfunctional or die during disease, such as stroke or Alzheimer’s disease. Our research focuses on calcium signalling in pericytes, which is likely important for regulating blood flow. We want to know: what causes calcium signals in pericytes? What happens as a result of these signals? How does pericyte calcium signalling change with age or during Alzheimer's disease? These questions are fundamental for understanding pericyte physiology and their role in brain health. This work may also lead to future development of pericyte-specific drugs for therapeutic use.
Research area, student roles & skills
Research area: The Stobart lab studies astrocytes and pericytes within the neurovascular unit to better understand the roles of these cells in brain physiology and how they may change with age or during Alzheimer's disease.
Student roles: Students who join our energetic team will have the opportunity to use new genetic mouse tools to observe beautiful, never-before-seen calcium dynamics in pericytes in real-time. They will record movies of these signals by two-photon microscopy (the latest, state-of-the art microscopy technique in neuroscience) and learn to analyze these calcium movies (through a program called MATLAB). Students will also develop communication and problem-solving skills by participating in regular lab meetings in a group setting. Our lab is located in the Apotex Centre at the University of Manitoba Bannatyne Campus, a dynamic community that encourages interactions between scientists from various health research disciplines.
Skills required: Students should: - have knowledge/experience in the life sciences. - have good communication skills. - be able to work independently or as part of a team. - be motivated to learn new things.
14. cell communication in brain tumor metastasis
Supervisor: Thomas Klonisch
University: University of Manitoba (Winnipeg campus)
This project is embedded within a larger program on breast to brain metastasis (BCBM) and uses various slef-generated breast cancer cell models to investigate the molecular mechanisms of brain colonization and therapeutic resistance of BCBM. This includes studies on the role of cell-cell interactions and signaling events that promote drug resistance and anti-apoptosis/ proliferative mechanisms. We utilize sophisticated cell biology techniques (e.g., protein expression, detection and interaction assays, reporter assays) as well as mouse models of brain metastasis with tissue preparations and immunostaining methods.
Research area, student roles & skills
Research area: My expertise is in breast to brain metastasis (BCBM); we use in-vitro and in-vivo models to study cell-cell communication mechanisms among tumor cells and between BCBM tumor and resident brain cells.
Student roles: the selected student will be trained and work with a graduate student in the lab to acquire the necessary skills to do the work in her/his project. Once trained, the student is required to independently generate data and present them at regular lab meetings. If the student performs well she/he will have the opportunity to present their data at local meetings.
Skills required: The student must be committed to this BCBM project and will need to know the basics of immunodetection of proteins in tissue sections. This includes the ability to utilize high-end microscopes for imaging those tissues and processing/ quantification of image data.
15. optogenetics in brain tumors
Supervisor: Thomas Klonisch
University: University of Manitoba (Winnipeg campus)
Location: Winnipeg, Manitoba
Start date: 2027-05-03 (flexible)
Disciplines: Neuroscience, Molecular Biology, Medical Sciences
we use optogenetic methods to target brain tumor cells (primary glioma and brain metastatic cells) for therapeutic purposes. This work involves the use of optogenetic constructs for expression analysis in brain tumor cells prior to injection of these cells into mouse brain. The student would be working on the in-vitro characterization of optogenetically modified brain tumor cells.
Research area, student roles & skills
Research area: we explore the use of optogenetics as a tool to treat brain tumors
Student roles: the student is paired with a research associate and graduate student that train the student in the required techniques. The student is expected to follow existing standard operating procedures and report regularly on their progress to their direct supervisors and the lab director.
Skills required: the suitable student should have a basic understanding of molecular biology, cellular composition of the brain and brain structures, and be willing to learn microscopy. This is a demanding project that requires strong commitment and fast learning skills.