Context
The person will join the cross disciplinary project Loop (closed Loop neurOtechnologies: from sensOrs to aPplications). The project will implement an interdisciplinary strategy applied to the development of a mechanism-based therapy for drug-resistant hallucinations in patients with schizophrenia. The project will be structured around three main research pillars: 1) the algorithmic level of closed-loop systems (online signal processing, machine learning); 2) the design of a complete and non-invasive solution to address the question of refractory hallucinations, and study how closed-loop interaction schemes can be beneficial for highly impaired psychiatric patients; 3) the design of the sensors themselves and translational research with animal models, in order to enhance the quality of the recordings and pave the way for future invasive miniaturized solutions. In this context, the objective is to apply federated and decentralized learning techniques to learn predictive models.
Assignment
The objective is to adapt the declearn API to the specific case of FRMI and EEG data, time series and eventually to address the problem of federated learning in the neuromorphic paradigm. The work plan is the following
- Join the team that writes the data preparation pipeline. The challenges are the scalability with respect to data size, data dimensionality, and time dependent data. The declearn readers and data representation choices will be monitored and adapted to the new choices and constraints.
- Adapt and implement federated algorithms tailored to the learning problem specifications. A specific attention will be on the scalability and the efficiency of the communications.
- Extend DecLearn to the fully decentralized setting.
- Address the problem of privacy.
- Implement specific approaches for neuromorphic computation.
Main activities
- Develop code.
- Write documentation.
- Write tests.
- Launch experiments.
- Report activity in the consortium.
Skills
Technical skills and level required :
- a strong understanding of distributed algorithms
- software design and development skills (relevant code may include Python and Rust)
Languages :
Mastering English is essential
Relational skills :
- smoothly working in a team in a reseach environment
- effective communication and collaboration
- eager to learn in an academic setting
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Engineer F/M - Scientific programmer in federate learning with applications in neuroscience employeur : Inria Villeneuve d'Ascq
En tant qu'employeur, notre entreprise se distingue par son engagement envers l'innovation et la recherche de pointe dans le domaine de l'optimisation combinatoire. Nous offrons un environnement de travail collaboratif où les chercheurs peuvent développer leurs compétences tout en contribuant à des projets significatifs sur des plateformes de calcul haute performance. De plus, notre culture d'entreprise valorise la croissance personnelle et professionnelle, avec des opportunités de publication et de collaboration internationale.
Coordonnées :
Équipe de recrutement de Inria Villeneuve d'Ascq
