Postdoc in Neural Decoding and Closed-Loop neuromodulation …, Lausanne

Mission We are seeking a highly motivated postdoctoral researcher to develop next‑generation neural decoding frameworks for multimodal motor and neural data in patients with movement disorders. 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 successful candidate will contribute to both methodological innovation and clinical translation, working closely with patients, clinicians, and a multidisciplinary research team.

Who We Are The SPARK laboratory for Adaptive Neuromodulation atÉcole 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. Our research combines engineering, neuroscience,and clinical translation to address three core objectives: (i) to comprehensively characterize mobility deficits across time, disease stages, and patient phenotypes; (ii) to uncover the neural mechanisms underlying gait and balance impairments; and (iii) to design predictive algorithms that adaptively trigger and regulate stimulation in real time. Embedded within the Neuro‑X Institute at EPFL and the NeuroRestore Center at CHUV–UNIL‑EPFL, the lab benefits from a uniquely translational ecosystem spanning neurosurgery, neurology, neuroscience, and neuroengineering, fostering close partnerships with MedTech companies such as Medtronic and ONWARD Medical. Over the past decade, our team has established a unique experimental platform combining high‑resolution whole‑body kinematics, muscle activity recordings, and large‑scale neural signals (subcortical and cortical recordings). This integrated approach enables the development of personalized, activity‑dependent therapies across neurological conditions, including Parkinson’s disease, spinal cord injury, and stroke. EPFL provides a world‑class environment in engineering, computation and neuroscience, enabling the development of advanced algorithms and real‑time systems for real‑world deployment. These capabilities are tightly integrated with CHUV’s clinical infrastructure, one of Switzerland’s five university hospitals and the only center in the French‑speaking region performing deep brain stimulation formovement disorders. The position will be based at Campus Biotech in Geneva and will involve regular interactions with EPFL and CHUV teams in Lausanne.

Main Duties And Responsibilities

Develop and implement state‑of‑the‑art neural decoding frameworks for multimodal motor and neural datasets

Identify and validate clinically relevant neural biomarkers across therapeutic conditions

Contribute to the development of closed‑loop and adaptive neuromodulation strategies, and to the deployment of next‑generation neuromonitoring platforms integrating high‑resolution kinematics, neurobiomechanical modeling, and neural decoding for closed‑loop therapy delivery

Integrate neural decoding with wearable sensing systems for real‑world, unconstrained monitoring

Design and conduct experiments in close collaboration with clinicians and patients

Supervise MSc/PhD students and contribute to the coordination of ongoing projects within the team

Contribute to high‑impact publications and to grant writing, scientific dissemination, and the development of collaborations across EPFL, CHUV, and partner institutions

Help coordinate laboratory activities and support the structuring of a collaborative and high‑performing research environment

Profile

You hold a PhD in neurotechnology, neural engineering, computational neuroscience, brain‑computer interfaces, or a closely related field

You have strong expertise in machine learning for neural data, with demonstrated experience applying modern approaches to electrophysiological signals such as LFP, EEG, or related modalities, including for real‑time applications

You have experience working with multimodal datasets across timescales, integrating neural activity with behavior (e.g., kinematics, EMG, wearable sensors), and a clear interest in decoding real‑world motor states

You are proficient in Python (preferred) and/or MATLAB, with experience in modern machine learning frameworks (PyTorch, TensorFlow, scikit‑learn), and strong skills in signal processing, data pipelines, and reproducible research practices. Experience with real‑time or embedded systems is a plus

You have hands‑on experience with human electrophysiology (e.g., LFP recordings from implanted devices, EEG), and are comfortable designing and implementing experimental paradigms in clinical populations

You bring knowledge of Parkinson’s disease pathophysiology and motor control, allowing you to identify relevant neural biomarkers and guide hypothesis‑driven analyses

You are motivated to bridge controlled experimental settings and unconstrained monitoring, including integration with wearable technologies for ambulatory assessment

You are able to work independently, structure complex projects, and troubleshoot across experimental, computational, and clinical domains

You have strong interpersonal and communication skills, and are comfortable interacting with patients and their families in a clinical research context

You have experience (or strong motivation) to supervise students and coordinate small project teams

You are fluent in English. Knowledge of French is desirable for patient interaction and clinical integration

We offer

A stimulating and international working environment

Competitive salary and excellent working conditions– more information can be found on our website: to perform state‑of‑the‑art research in one of the most dynamic scientific institutions in Europe

Opportunity to interact with internationally renowned experts, and with a strong team of postdoctoral researchers and PhD students

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