Engineer F/M - Scientific programmer in federate learning with applications in neuroscience

Inria, the French national research institute for the digital sciences; France; Hybride; EUR 42 000 - 64 000; Plein temps

Avantages offerts par ce poste

Partial public transport reimbursement

7 weeks of annual leave + RTT days

Teleworking options

Professional equipment available

Résumé du poste

Inria is seeking a researcher to join the Loop project, focusing on federated learning for FRMI/EEG data, online signal processing, and distributed algorithms. The role combines data engineering, algorithm design, and software development within a multidisciplinary academic setting.

You will adapt federated methods to neuromorphic time-series data, implement scalable solutions, and collaborate with teams across computer science and neuroscience.

Qualifications

• Experience with distributed algorithms and federated learning.
• Ability to work in an interdisciplinary academic setting.
• Strong programming skills in Python and Rust.

Responsabilités

• Join the team that builds the data preparation pipeline for large-scale data.
• Adapt and implement federated algorithms tailored to project specs.
• Extend DecLearn to decentralized settings and address privacy considerations.
• Work on neuromorphic-inspired computation and time-series data processing.
• Demonstrate strong understanding of distributed algorithms and software design in Python/Rust.

Connaissances

Teamwork

Communication

Learning mindset

Python

Rust

Outils

Python

Rust

Description du poste

Inria, the French national research institute for the digital sciences

Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Application Deadline 18 Nov 2026 - 00:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 1 Mar 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 2026-10623 Is the Job related to staff position within a Research Infrastructure? No

Offer 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.

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.
• a strong understanding of distributed algorithms
• software design and development skills (relevant code may include Python and Rust)

Languages :

Relational skills :

• smoothly working in a team in a reseach environment
• effective communication and collaboration
• eager to learn in an academic setting

Languages FRENCH Level Basic

Languages ENGLISH Level Good

Additional Information

• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities