Sigma Nova is recruiting M2 / PhD research interns to work on foundation models for brain signals, with a focus on robust generalization under strong data shift (cross-subject, cross-session). Our team has won the silver medal on 2025 NeurIPS EEG Challenge. A few key points make this opportunity different,

• You’ll work primarily with EEG, with opportunities to connect to other modalities (MEG/iEEG/fMRI)
• You’ll do publication-oriented foundation model research at the intersection of machine learning and neuroscience
• You’ll have access to large scale compute, and large amounts of data, including in-house data acquired through partnerships

Internship Description

Artificial Intelligence (AI) models are frequently deployed in contexts different from the well behaved, clean datasets they were trained on. In the literature, this phenomenon is known as distribution shift, a case where the underlying probability distribution from test data differs from the training data.

The distribution shift problem occurs frequently in Brain Computer Interfaces (BCIs), where training data is composed of a heterogeneous ensemble of readings from different subjects, and must generalize to unseen subject. This setting poses the well known cross-subject variability in EEG data, that is, models have to cope with the inherent heterogeneity of the training, and test data.

This internship seeks to study the impact of cross-subject induced distribution shifts in EEG Foundation models. The intern will select one of the possible research lines to follow,

1. New pre-training strategies
2. New fine-tuning strategies
3. New in-context learning strategy
4. New data generation strategies tailored for EEG

In summary, this internship is at the intersection of domain adaptation, model weight space learning and interpretability of foundational models, intersecting every step of the large scale model life-cycle pipeline.- Strong Python skills; experience with PyTorch

• Solid ML fundamentals:

  • Deep Learning
  • Representation Learning

• Desirable, but not mandatory:

  • Time-series
  • Optimal Transport

• Comfortable reading papers, running experiments, and writing clean, reproducible code

What we offer

• A research-driven startup environment at the interface of AI & neuroscience
• Close mentorship with room to shape the project within the chosen track
• A collaborative environment with various experienced researchers in machine learning and neuroscience
• Opportunity to contribute to open-source artifacts and/or a research paper
• Hybrid position (two days of remote work a week), based in Paris
• A high-end computational infra-structure and access to large amounts of data1) Prescreen with Recruiter

2. Hiring meeting with one Research team member

3. Onsite interview (coding and research discussion)