Opportunities

Fellowships

IVADO Postdoctoral Fellowship Program 2023: We are looking for candidates willing to apply to this unique opportunity to realize a postdoc at McGill University, Canada to join the MyoSuite team and work on AI in computational neuroscience & healthcare. The fellowship offers a 75k$ salary and 30K$ research fund per year.

Contact Guillaume Durandau at guillaume.durandau@mcgill.ca to discuss this opportunity.

Timeline:

More info: https://ivado.ca/en/scholarships-and-grants/ivado-postdoctoral-fellowship-program-2023/ 

[Half-funded] PhD studentship: Incorporation of High-level inputs in MyoHand

We are seeking potential industry collaborators who are interested to sharing the studentship of a 3.5-year tuition program, totalling £52.5k, throughout the entire tenure of a student's engagement in the Myosuite project. Irrespective of their organizational scale, any company is welcome to submit a project proposal in partnership with our academic collaborator at King's College London. These collaborative ventures require the participating company to contribute 50% of the studentship's cost, amounting to £52.5k for a resource spanning three years. Co-funded iCASE projects will undergo rigorous evaluation through a peer review process upon submission. More information on the opportunity can be found here: https://sites.google.com/view/gionfrida/opportunities 


Internships

[Undergrad Summer Internship]: Simulation of a neuromusculoskeletal model of the back with an exoskeleton

Back exoskeletons are one of the main research axes for reducing injury in the workforce but it is unknown what the effect of mechanical force on the spine and how to design useful exoskeletons for the back. This is due to the complexity of the spine neuromusculoskeletal system which requires multiple design iterations and tests on real subjects. One way to accelerate the design of back exoskeleton and create efficient and useful assistance for reducing injury is by simulating both the spine neuromusculoskeletal system and exoskeleton and looking at how they influence each other. This is why this project aims to develop a new back and exoskeleton model in MyoSuite (https://sites.google.com/view/myosuite) and validate it against experimental data previously collected. All results and developed models and code will be made open-source at the end of the project.

McGill University, Montreal, Canada. Funded by Mitacs. Participants will be eligible for a Globalink Graduate Fellowship for doing a Master's or PhD in Canada.

How to apply details, Internship details (in Faculty last name put: Durandau)
Deadline: September 21, 2023, at 1 p.m. Pacific Time (PT).


Internship 2: Reinforcement learning-based control of a neuromusculoskeletal model of the back with an exoskeleton

Back exoskeletons are one of the main research axes for reducing injury in the workforce but it is unknown what the effect of mechanical force on the spine and how to design useful exoskeletons for the back. This is due to the complexity of the spine neuromusculoskeletal system which requires multiple design iterations and tests on real subjects. One way to accelerate the design of back exoskeleton and create efficient and useful assistance for reducing injury is by simulating both the spine neuromusculoskeletal system and exoskeleton and looking at how they influence each other. This is why this project aims to develop a policy trained using reinforcement learning for the control of neuromusculoskeletal models wearing a passive exoskeleton. The policy will be able to reproduce unknown (i.e. not used for the training or the reward shaping) experimental data previously recorded by just following the kinematics cue.

McGill University, Montreal, Canada. Funded by Mitacs. Participants will be eligible for a Globalink Graduate Fellowship for doing a Master's or PhD in Canada.

How to apply details, Internship details (in Faculty last name put: Durandau)
Deadline: September 21, 2023, at 1 p.m. Pacific Time (PT).


Internship 3: Studying step response in balance using reinforcement learning

Fall is one of the main contributors to loss of mobility in the older population. Injuries caused by falls can reduce the quality of life and even cause death. Defining a new robotic device and controller for increased balance recovery and preventing falls will have a huge impact int he quality of life in the elderly population. Unfortunately, testing devices on the targeted groups is challenging. For this neuromusculoskeletal predictive simulations have been developed where experiments can be conducted in simulation. We previously developed a neuromusculoskeletal model of the lower limb driven by a policy trained using reinforcement learning that can sustain low perturbation. We would like to look at the possibility of sustaining higher perturbation and generating a stepping response. Stepping response is a balanced recovery strategy where we step forward to regain stability. Creating simulations that use natural and biological balance strategies is a step forward towards the creation of better predictive stimulation that will help the elderly population regain autonomy.

McGill University, Montreal, Canada. Funded by Mitacs. Participants will be eligible for a Globalink Graduate Fellowship for doing a Master's or PhD in Canada.

How to apply details, Internship details (in Faculty last name put: Durandau)
Deadline: September 21, 2023, at 1 p.m. Pacific Time (PT).