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# Engineer F/M - Scientific programmer

- **Company:** Inria
- **Location:** Villeneuve-d'Ascq, France (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Python (Programming Language), Machine Learning, Signal Processing, Data Representation
- **Published:** October 20, 2026
- **Apply:** https://jobs.inria.fr/public/classic/fr/offres/2026-10623

## About the Role

* 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

## Description

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.

Mission confiée

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.

Principales activités

 * Develop code.
 * Write documentation.
 * Write tests.
 * Launch experiments.
 * Report activity in the consortium.