Research Engineer M/F in Auditory Neuroscience (MEG data analysis and modelling)

Job Description

Missions

Within the ERC-Synergy project NASCE, you will investigate how the human brain represents isolated natural sounds using magnetoencephalography (MEG), how these representations evolve over time, and how they compare with acoustic, semantic, and deep-learning models.
Your work will focus on:
- Completing and extending analyses of existing MEG datasets collected with isolated natural sounds, at sensor level and in source space;
- Applying representational similarity analysis (RSA) and partial information decomposition (PID) to compare brain responses with acoustic models, text-based semantic models, and deep neural networks;
- Contributing to the development of robust, reusable Python pipelines for the NASCE consortium and to the dissemination of results (papers, abstracts, presentations).

Activity

• Pre-process MEG data (artefact rejection, filtering, epoching, quality control);
• Perform source reconstruction and region-of-interest analyses using MNE-Python;
• Use and evaluate deep neural networks for sound processing, extracting and comparing representations across their layers;
• Build and analyse representational dissimilarity matrices (RDMs) from brain data and computational models;
• Implement cross-validated RSA, mutual information estimation, PID, and associated statistical tests to quantify unique, redundant, and synergistic information and its evolution over time;
• Document and maintain analysis code in version-controlled repositories (Git);
• Prepare figures, results tables, and text for manuscripts and conferences.

Your Profil

Skills

Education / experience: Master's degree (or equivalent) in neuroscience, cognitive science, biomedical engineering, computer science, data science, or a related field. Prior experience with human MEG/EEG data analysis is required; a PhD in one of these areas is a strong asset.
Technical skills: Very good command of Python for scientific computing (NumPy/SciPy, MNE-Python, scikit-learn or similar), good knowledge of statistics, information theory (mutual information, PID), and multivariate methods (RSA, encoding models, model comparison), experience using and analysing deep neural networks, and familiarity with Linux environments and version-control tools.
Domain knowledge & personal qualities: Strong interest in auditory or sensory neuroscience; autonomy, scientific rigour, and good organisational skills; ability to work in a team and interact within an international consortium; very good written and spoken English (French is a plus but not mandatory).

Your Work Environment

The position is hosted at the Institut de Neurosciences de la Timone (INT) in Marseille, within the team led by Bruno Giordano. You will interact with researchers, engineers, and PhD students working on auditory neuroscience, MEG/iEEG, and computational modelling, and collaborate with the European NASCE project partners (notably the Maastricht group for ultra-high-field MRI and AI-based modelling).

Constraints and risks

NA

Compensation and benefits

Compensation

2 571,89 € – 3 817,32 € gross/month (depending on experience)

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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