Postdoc In Neural Decoding And Closed-loop Neuromodulation For Neurodegenerative Disorders

EPFL - EPF Lausanne

Geneva, Switzerland
Base: competitive salary; bonus/equity: not specif...
On-site
Machine learning for neural data
Multimodal datasets integration
Python and/or matlab proficiency
The École Polytechnique Fédérale de Lausanne (EPFL) is seeking a postdoctoral researcher to develop advanced neural decoding frameworks for movement disorder patients. The role involves working with multimodal neural data, applying machine learning techniques, and collaborating with clinicians in a clinical research environment

Job Summary

  • The project focuses on leveraging state-of-the-art machine learning approaches to predict and quantify changes in motor and cognitive function, while integrating electrophysiology (e.g., LFP, EEG) with wearable sensing for real-world, unconstrained monitoring.
  • The SPARK laboratory for Adaptive Neuromodulation at École Polytechnique Fédérale de Lausanne (EPFL) and at the University Hospital Lausanne (CHUV) is developing next-generation closed loop neuromodulation therapies to restore mobility in patients with Parkinson’s disease and related disorders.
  • EPFL provides a world-class environment in engineering, computation and neuroscience, enabling the develop-ment of advanced algorithms and real-time systems for real-world deployment.

Matching Summary

Match Score: 85

The École Polytechnique Fédérale de Lausanne (EPFL) is seeking a postdoctoral researcher to develop advanced neural decoding frameworks for movement disorder patients. The role involves working with multimodal neural data, applying machine learning techniques, and collaborating with clinicians in a clinical research environment.

Salary

Base: Competitive salary; Bonus/Equity: Not specified; Benefits: excellent working conditions

Skills & Requirements

Must-have

  • Machine learning for neural data
  • Multimodal datasets integration
  • Python and/or MATLAB proficiency
  • Human electrophysiology experience
  • Clinical populations experimental design

Nice-to-have

  • Real-time or embedded systems experience
  • Knowledge of Parkinson's disease
  • French language proficiency
  • Supervising students and teams

Key Requirements

  • PhD in neurotechnology, neural engineering, computational neuroscience, brain–computer interfaces, or related field
  • Experience applying modern ML to electrophysiological signals
  • Interest in decoding real-world motor states
  • Proficiency in signal processing and data pipelines
  • Ability to work independently and troubleshoot complex projects

Work Rights

Not specified

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