[Comp-neuro] Postdoc position in computational modelling at EPFL (Lausanne, Switzerland)
tomislav.milekovic at epfl.ch
Sun Aug 9 11:45:04 CEST 2020
Postdoc position in the lab of Prof. Gregoire Courtine at EPFL (Lausanne, Switzerland)
Data-driven computational modelling to develop and enhance clinical treatments based on electrical stimulation of the spinal cord
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.
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.
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. Yet, clinically available electrode arrays used to deliver the EES were not designed to target individual spinal roots. Data driven design of the electrode arrays has the potential to substantially improve the specificity of spinal EES and, therefore, dramatically improve the recovery of patients. Pre-operative planning and intra-operative assistance based on accurate models of the spine can de-risk the surgery needed to implant the electrode arrays and increase the efficacy of the EES-based therapies. The efficacy of EES can be further enhanced through computational algorithms capable of designing EES protocols that fully utilize the interaction between the electrode array and patient's anatomy. These developments are critical for deployment of the EES-based therapy to clinics around the world to help millions of people suffering from spinal cord injury and Parkinson's disease.
We have created a computational pipeline capable of creating detailed computational models of individual persons' spinal columns from CT, MRI and fMRI scans. These hybrid models are composed of:
* 3D finite element models (FEM) to characterize the electric current and potential in the spinal cord of individuals.
* Compartmental cable models to characterize numerous axon pathways that distribute the information from the spinal cord to rest of the body.
* Network models of spinal cord neuronal populations to calculate the effects of EES on the spinal networks and, in turn, the activation of muscles.
This computational approach has the potential to optimize the efficacy of EES on a personalized basis, lead to novel superior electrode array designs, and further our understanding of the mechanisms by which spinal cord controls movement.
The successful candidate will work to generate highly accurate models of individual human and animal model spines and apply those models to revolutionize the EES-based therapies. Specifically, he will:
* Use spinal models to generate new spinal electrode array designs, develop procedures for pre-operative planning and intra-operative assistance, and create methods that determine stimulation protocols from the spinal models.
* Lead the work to extend our lumbosacral spinal cord models to thoracic and cervical spinal regions.
* Integrate data from ever-more accurate invasive and non-invasive medical imagining and physiology techniques to enhance the accuracy of spinal models.
* Coordinate integration of new findings on spinal neuronal populations, spinal networks and spinal pathways into our spinal models.
* Assist and oversee the development of tools to automatize the process of spinal model generation.
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 computational modeling concepts that support 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.
* Doctoral degree (PhD)
* Proficiency in Python, Matlab and C++
* Experience with finite element models, compartmental cable models and neurobiomechanical models
* Experience in NEURON and/or NEST
* Experience in OpenSim, Mujoco or Webots
* Good written and verbal skills in English
Applications including a CV and a cover letter describing your background and interest should be sent to tomislav.milekovic at epfl.ch<mailto:tomislav.milekovic at epfl.ch>. Informal inquiries are welcome.
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