Students and Open positions

Master’s Thesis Project

Master Thesis Project (Microengineering, Mechanical engineering or Bioengineering)

SPINAL CORD STIMULATION TO RESTORE CYCLING AFTER PARALYSIS

Description

We are developing spinal electrical cord stimulation protocols to restore motor functions of people paralyzed due to spinal cord injury. In this context, we are interfacing the stimulation with a recumbent bike that enables outdoor cycling. Your objective is to contribute to this integration and its evaluation. This project will be a joint project under the supervision of the NeuroRestore center (EPFL/CHUV) and the Bern university of applied Science (BFH).

Objective

Develop stimulation protocols to adapt muscle activity to the cycling movement on a trike and personalize stimulation in patients with spinal cord injury.
Identify measures to evaluate the use of the trike with and without stimulation (trike logs and stimulation logs)
Analyze outcome measures from the trike (force sensors, IMU’s, power measures etc..) to quantify the effect of training with the trike with and without stimulation over time.
Identify and analyze physiological changes related to the use of the trike (EMGs, cardiac frequency, generated force).  
Adapt testing to an indoor cycling device for rehabilitation (BiPed Trike) in collaboration with a research team at UNIL (Dr. Jérôme Barral)
Evaluate the movement dependencies and performance to optimize the combinations of movements and stimulation.


Location

The candidate will work at our offices at the CHUV, Hôpital Nestlé, Avenue Pierre-Decker 5, 1011 Lausanne, Switzerland.

Contact

Applications including a CV, short motivation letter, and the names and full contact details of two referees should be sent to [email protected] and [email protected]

PostDoc position in applied machine learning techniques for control of spinal stimulation

PostDoc in applied machine learning techniques for control of spinal stimulation

Location

The laboratory of Prof. Gregoire Courtine at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland, is looking to fill a fully funded postdoc position. The qualified candidate will benefit from joining a very dynamic and multidisciplinary group working at the interface of computational neuroscience, neuroengineering, prosthetics and biology. EPFL provides state-of-the-art facilities and is one of the leading technical universities worldwide. Postdoc salaries at EPFL rank the highest in the world.

Opportunity

The offered position will be based at the Defitech Center for interventional Neurotherapies (NeuroRestore) – a research and innovation center joining EPFL’s lab of Prof. Gregoire Courtine and the University Hospital of Lausanne (CHUV) lab of Prof. Jocelyne Bloch. NeuroRestore conceives, develops and applies medical therapies aimed to restore neurological functions. To this end, NeuroRestore integrates implantable neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. By working with our network of vibrant high-tech start-ups and established medical technology companies, NeuroRestore is committed to validate our medical therapy concepts. The overarching goal of NeuroRestore is to see our medical therapies used every day in hospitals and rehabilitation clinics worldwide.

Project Description

Therapies based on epidural electrical stimulation (EES) of the spinal cord can restore the ability to walk to people paralyzed by spinal cord injury, and alleviate gait deficits of people with Parkinson’s disease. EES does this by recruiting sensory axons within dorsal spinal roots that enter the spinal cord between the vertebrae to increase the activation of the spinal motor pools that, in turn, move the muscles. Yet, the efficacy of the EES-based therapies relies on synchronizing users’ movement intentions with the spatiotemporal stimulation protocols that reliably and accurately generate paralyzed movements. Due to the large state space of all the stimulation parameters (location, amplitude, frequency, etc.) efficacy of the therapy depends on the fast and accurate initialization of the stimulation protocols. As the patients use the stimulation, small movements of the array, as well as changes in spine position due to users’ posture can reduce the usability of the stimulation. Stimulation efficacy can be enhanced by dynamically adjusting the stimulation protocols to changes to the way how to users’ spinal cord reacts to stimulation. Finally, the functional use of the stimulation largely depends on the accurate timing of stimulation delivery. Machine learning approaches that infer users’ intentions based on behavioral, physiological or neural recordings can vastly improve the synchronization between intended and therapy-supported movements and, therefore, play a critical role in achieving functional recovery of patients. While the current medical devices mostly support block-based stimulation.protocols that remain constant for hundreds of milliseconds, upcoming devices will enable changes of stimulation at a millisecond resolution, thus opening a new field for machine learning approaches that exploit these capabilities.

The successful candidate will work to develop, implement and apply machine learning algorithms and approaches to enhance EES-based therapies. Specifically, he will:

  1. Design the mapping procedures for the generation of transfer functions that relate the continuously-controlled stimulation to the evoked muscle activity.

  2. Develop algorithms that automatically adjust these transfer functions as the interaction between patients and their EES-based therapy evolves.

  3. Implement machine learning techniques that utilize users’ behavioral, physiological and neural signals to continuously synchronize the delivery of stimulation with the users’ movement intentions.

  4. Lead the team that develops machine learning methods to initialize and adjust block-based EES protocols.

  5. Assist and oversee the development and implementation of machine learning methods that use inference of discrete motor events to synchronize block-based EES protocols with the users’ intentions.

By integrating well-equipped and expertly staffed rodent, non-human primate and clinical research facilities, NeuroRestore provides an ideal substrate for rapidly developing, integrating and clinically validating cutting-edge machine learning concepts within medical therapies, with the capacity to push successfully proven concepts into the technology transition phase. The successful candidate will have access to these animal platforms and will work within the framework of multiple NeuroRestore clinical trials with people with spinal cord injury and Parkinson’s disease. They will benefit from the possibility of validating their concepts in animal experiments and implementing them within the therapies being tested in the clinical trials.

Requirements

  • Doctoral degree (PhD)

  • Proficiency in Python, Matlab and C++

  • Strong background in quantitative data analysis

  • Experience with applying multiple machine learning techniques to behavioral, physiological, biological and/or neural datasets

  • Good written and verbal skills in English

Contact

Applications including a CV and a cover letter describing your background and interest should be sent to [email protected]. Informal inquiries are welcome.

Master’s Thesis Project

A computational approach to study control and execution of locomotion.

Description:

Locomotion is one of the most fundamental skills of humans and animals alike. Yet, it is an extremely complex task that requires a finely tuned composition of neural signals to activate numerous muscles distributed across the entire body. The underlying system of nervous structures that control locomotion can be roughly divided into three components:

  • Peripheral sensory system that collects the information about the environment and the state of the body.

  • Spinal sensorimotor circuits that receives the sensory inputs and, in response, directly control the muscles to generate synchronized locomotor movements.

  • Brain’s locomotor system that orchestrates the locomotor behaviour by modulating spinal circuits based on internal goals and sensory inputs.

Computational and experimental studies enabled the deconstruction of the fundamental components of the first two systems. However, the exact role of the brain in generating and maintaining locomotion remains unclear. The central area of the brain’s locomotor system is the motor cortex that receives sensory inputs and movement goal form other brain areas and sends direct inputs to the spinal locomotor circuits. In this Master’s Thesis, we aim to study the interaction between the motor cortex and the spinal sensorimotor circuits to enable locomotion. For this purpose, the student will develop on a computational model of primate locomotion that is divided into a data-driven representation of the motor cortex, a spiking network abstraction of spinal sensorimotor circuits and a biomechanical model of primate locomotion.

Prerequisites:

  • Experience in Machine Learning with Python
  • Experience in NEURON and/or NEST
  • Experience in OpenSim, Mujoco or Webots

Location:

The project can be carried out remotely.

Contact:

In case of interest, please contact us via: [email protected]

Software development internship

NeuroRestore is a research and innovation center spanning EPFL and the University Hospital of Lausanne (CHUV) that develops and applies medical therapies aimed to restore neurological functions. We integrate implantable neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. These developments have led to breakthroughs for the treatment of paraplegia, tetraplegia, Parkinson’s disease, stroke, and traumatic brain injuries. By working with our network of vibrant high-tech start-ups and established medical technology companies, we are committed to validate our medical therapy concepts and see them used every day in rehabilitation clinics worldwide.

The position:

We are looking for a motivated and dynamic candidate for an internship focused on development of a versatile software platform that regulates spinal cord stimulation using signals from other medical sensors. They will learn to develop effective software modules and write code according to the best coding practices and regulatory requirements linked to medical software. This includes implementing new features and improvements to the software platform, performing and implementing unit and integration tests and completing the required documentation. The candidate will work in a highly attractive, multidisciplinary and international environment spanning high-tech medical industry, academic labs, and clinical centres. They will benefit from interacting with worldwide scientific experts in cybernetics, neurotechnology and rehabilitation.

The ideal candidate will have:

  • Good coding skills in C / C++ / C#.
  • Experience with coding in an object oriented style.
  • Experience with coding according to style guides.
  • Fluent use of English.
  • Experience with team software development and relevant tools (SVN, GIT, etc.).
  • Ability to learn fast.
  • Affinity to work in a dynamic and effective team.

The following will also be a plus:

  • Experience in graphical interface development
  • Experience in medical software development
  • Experience in interfacing with hardware APIs

The qualified candidate will benefit from joining a dynamic and multidisciplinary group working at the interface of software development, engineering, neuroscience, prosthetics and cybernetics. They will collaborate with EPFL academic labs, CHUV clinicians and vibrant medical high-tech start-ups and established medical companies. The candidate will have the opportunity to work at the interface between academia, industry and the clinic with collaborators from different backgrounds and cultures, including worldwide scientific experts in cybernetics, neurotechnology and rehabilitation.

Location:

The candidate will work at our CHUV offices at Hôpital Nestlé, Avenue Pierre-Decker 5, 1011 Lausanne, Switzerland.

Contact:

Applications including a CV, short motivation letter, and the names and full contact details of two referees should be sent to [email protected].