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

Within the ERC-Synergy project NASCE, you will investigate how the human brain represents isolated natural sounds using magnetoencephalography (MEG), and how these representations relate to acoustic, semantic, and deep-learning models. Your work will focus on:

• Completing and extending source-space analyses of existing MEG datasets collected with single natural sounds;
• Applying representational similarity analysis (RSA) and variance-partitioning techniques to compare brain responses with acoustic features, 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).

Activities

• Curate and pre-process MEG data (artefact rejection, filtering, epoching, quality control);
• Perform source reconstruction and region-of-interest analyses using MNE-Python and associated tools;
• Build and analyse representational dissimilarity matrices (RDMs) from brain data and computational models;
• Implement cross-validated RSA, variance partitioning, and related statistical tests;
• Document and maintain analysis code in version-controlled repositories (Git);
• Prepare figures, results summaries, and texts for manuscripts and conferences.

Education / experience: Master's degree (or equivalent) in neuroscience, cognitive science, biomedical engineering, computer science, data science, or related field. Experience with human MEG/EEG data analysis is required; a PhD in one of these areas is a strong asset. Technical skills: Solid Python skills for scientific computing (NumPy/SciPy, MNE-Python, scikit-learn or similar), good command of statistics and multivariate methods (RSA, encoding models, model comparison), experience with Linux work environments and version control. Domain knowledge & personal qualities: Clear interest in auditory or sensory neuroscience; ability to work independently and as part of a team; scientific rigour and good organisational skills; very good written and spoken English (French is a plus but not mandatory).