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Host laboratory

The Biomedical Imaging Laboratory (Laboratoire d’Imagerie Biomédicale = LIB), affiliated with INSERM / CNRS / Sorbonne University.
Sorbonne Université – Campus des Cordeliers; 15 rue de l’Ecole de Médecine; 75006 Paris, France

Duration and the starting date

36 months, full time employment, starting November 2025

Application deadline

July 31, 2025 (if a good candidate is found earlier, we won’t accept more applications, so don’t wait until the last moment)

Qualifications expected

Master degree (or equivalent) in one of the following fields: Neuroscience, Biomedical Engineering, Computer Science, Applied Mathematics, Cognitive Psychology. Applicants with other master-level degrees from other disciplines may be also considered provided they have necessary skills (see below).

Context (Why studying this makes sense)

Motor adaptation is a crucial process in human life, enabling us to adjust our movements based on sensory feedback. For instance, imagine being asked to play your favorite game of petanque with a set of balls that are heavier than usual. You would need several trials to adapt your movements to achieve your baseline performance. This adaptation relies on the sensory prediction error—the difference between the intended and actual outcomes on each trial. Understanding motor adaptation has broad applications, from rehabilitation in patients after brain or body injuries to optimizing motor skills in sports, work, and robotics (motor adaptation is essential for advanced dexterity).

Despite its ubiquity and importance, motor adaptation is still not fully understood, especially at the neural level.

PhD Project Mission

This project aims to uncover neural mechanisms of motor adaptation. The tools will include advanced machine learning, mathematical modeling (dynamical systems) on existing data and conducting experiments on healthy participants performing motor tasks with simultaneous EEG recordings.

Objectives

The student will work towards achieving the following aims:

• Aim 1: Develop a Predictive Model of Motor Adaptation: Create an explainable machine learning pipeline capable of predicting movement in a multi-trial motor adaptation experiment involving a joystick, using existing data (multiple datasets)
• Aim 2: Design and Implement a Real-Time BCI System: Develop a real-time, closed-loop, passive Brain-Computer Interface (BCI) system, leveraging the ML models developed in Aim 1, to modulate task parameters and optimize motor adaptation.
• Aim 3: Validate BCI-Enhanced Motor Adaptation: Evaluate whether the BCI system developed in Aim 2 can enhance motor adaptation by conducting an experiment with healthy participants.

Depending on the candidate’s profile the project work will cover all or a subset of these aims.

Expertise/professional development the student will get from working on the project

Advanced Python programming, cutting edge deep learning methods for multivariate noisy time series analysis, explainable AI, NeuroAI, applied dynamical systems, control theory, digital signal processing, computational neuroscience, neural data analysis (EEG and MEG), knowledge of human motor control and mechanisms of motor network functioning, cognitive neuroscience, brain-computer interfaces. The student will attend schools/conferences on these topics and submit article(s) to international peer-reviewed journals on these topics. The student will also participate in the supervision of master (M2) students internships working on related projects. Gained experience will help the student to continue their career either in academia or in industry (ML/AI or BCI).

Required skills

Curiosity and creativity, Python, machine learning, basic signal processing, basic understanding of ordinary differential equations (ODEs), good command of English, scientific rigor, personal time management. Minimal understanding of neuroscience is desirable but not strictly necessary (in this case the desire to get this understanding is necessary).

Preferred skills

deep learning, experience with EEG experiments and EEG data processing, understanding of human motor control (inc. practical, such as experience in kinesiotherapy / rehabilitation / occupational therapy / professional sports coaching / professional dance teaching ), explainable AI, computational / cognitive neuroscience, statistics, ability to use LLMs for research in a smart way. A decent level of French is required if the student will collect the data themselves (but is optional if they will only supervise the data collection performed by a master student).

Host lab description

Biomedical Imaging Laboratory concentrates experts in different modalities of biomedical imaging, including a large team on neuroimaging. It includes permanent researchers with ample experience in MEG/EEG data analysis, BCIs, signal processing, deep learning for brain imaging analysis, biomedical statistics, dynamical systems and research on motor control. The lab has two locations: one in the very center of Paris (10 min walking from the Notre Dame de Paris cathedral, the student will work mainly there) and one in Pitié-Salpetriére hospital in the rehabilitation department. LIB has ample collaborations with other neuroscience institutions in Paris, notably with Paris Brain Institute (ICM). The principal investigator has extensive experience on the topic of the project and thus the student will not start from zero but rather will be able to benefit from already accumulated knowledge and tools.

Monetary compensation

Standard French PhD compensation.

How to apply

Send an email to the PI email below, attaching

1. CV,
2. notes from the master degree classes (if available),
3. PDF of the master thesis.

A motivation letter is not strictly required, but is desirable, especially if you have an atypical CV. It should explain why you are interested in this position and why you are a good fit for it (note that applications with naively LLM-motivation letters go to the trash bin right away). Two reference letters will be requested if the candidate passes the initial screening.

Contact of the PI

Dmitrii Todorov, PhD; Chaire Professeur Junior INSERM;
dmitrii.todorov #at# inserm.fr
Head of Cognitive Neuroscience of Motor System research group (CoNeMot),
part of Neural Connectivity and Plasticity (NCP) team.