Download the internship proposal

Stage type

Master 2 Neuroscience or Biomedical Engineering (Master 2 or last year of an engineering school in biomedical engineering or signal processing or electronics)

Date and duration of the internship

6 months, from February to July 2027 (with some dates flexibility, starting in December 2026 to simplify PhD applications later is possible).

Context

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 (darts is also a good example). You would need several trials to adapt your movements and achieve the same performance. Understanding motor adaptation has broad applications, from rehabilitation in patients after brain or body injuries to optimizing motor skills in sports, work, and robotics.

Despite its ubiquity, motor adaptation is still not fully understood at the neural level. A particular topic of interest is neural representation of contextual information during motor adaptation. Namely, how does one transfer learned behaviours between distinct tasks and movements? E.g. how exactly does it work that one knows how to throw an unfamiliar object to an unfamiliar target, generalizing previous experience with other similar objects and targets?

Objective

The objective of this internship is to simultaneously collect behavioral and electroencephalography (EEG) data from healthy participants performing a motor adaptation task, as well as conduct basic analysis of this data.

Missions

the intern will
● Read the literature on motor adaptation and its neural signatures
● Extend Python code for the experimental script (relatively minor changes are needed to an already existing code)
● Set up the data collection pipeline
● Recruit healthy human participants
● Collect EEG + behavior data at the lab premises under the supervision of the PhD student Yassine Lakhdari and the PI
● Organise collected data
● Perform statistical analysis of the data in Python, extending previous existing scripts
● (Optional, time permitting) Perform advanced machine learning analysis of the data in Python
● Gain experience in cognitive and motor neuroscience research, python programming, brain computer interfaces, enhancing their skills and CV for future academic research or ML/AI industry opportunities.

Skills required

Basic neuroscience, basic understanding of electronics (for EEG), Python, basic statistics, good level of French (to work with participants). Desired: computational neuroscience, basic signal processing, understanding of motor control, machine learning, dynamical systems

Monetary compensation

Internship compensation (according to current regulations).

Contact

send email to the following two addresses
1) Yassine Lakhdari, PhD student; yassine.lakhdari[at]inserm.fr
2) Dmitrii Todorov, PhD; Chaire Professeur Junior; dmitrii.todorov[at]inserm.fr,
Neural Connectivity and Plasticity (NCP) team

Please put [STAGE LIB CONTEXT EXP SPRING] in the subject of your email and write the email in English. Before applying check the online version for changes (link here).

Host laboratory

Laboratoire d’Imagerie Biomédicale, LIB, Sorbonne Université, Hôpital de la Pitié-Salpêtrière et Campus des Cordeliers, 15 rue de l’École de Médecine, 75006 Paris.