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

Inria Villeneuve d'Ascq; Villeneuve-d'Ascq; On-site; EUR 42,000 - 66,000; Full time

Job summary

Inria Villeneuve d'Ascq is seeking a researcher to adapt and extend the declearn API for FRMI/EEG time-series data, with a focus on federated learning in a neuromorphic setting. You will join a cross-disciplinary Loop project and contribute to scalable data pipelines, decentralized learning algorithms, and privacy-aware methods.

The role emphasizes code development, documentation, testing, and experimentation within an academic research environment, collaborating across teams and reporting

Qualifications

• Strong understanding of distributed algorithms.
• Software design and development skills; Python and Rust recommended.
• Mastering English is essential.
• Teamwork and clear communication in an academic setting.

Responsibilities

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

Skills

Distributed algorithms

Python

Rust

English

Job description

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