Engineer on private and decentralized machine learning for medicine (H/F)

Type de contrat : CDD

Contrat renouvelable : Oui

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Ingénieur scientifique contractuel

The Inria University of Lille centre, created in 2008, employs 360 people including 305 scientists in 15 research teams. Recognised for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria University of Lille centre pursues a close relationship with large companies and SMEs. By promoting synergies between researchers and industrialists, Inria participates in the transfer of skills and expertise in digital technologies and provides access to the best European and international research for the benefit of innovation and companies, particularly in the region.

The work concerns a project of the INRIA-MAGNET team with the Lille Hospital, in particular the development of the open source library DecLearn for private and decentralized/federated machine learning and data analysis to conduct multi-centric medical studies across several hospitals.

The activities will include interactions with the members of the Magnet team (researchers and engineers) and with engineers and doctors of the Lille Hospital. The activities can also include travel, e.g., to conferences to demonstrate the developed library and to contribute to the community building effort.

• Consolidate and extend the existing library for decentralized and privacy-preserving machine learning developed in the project
• Integrate these developments to the information systems of partners hospitals to enable decentralized medical studies
• Deploy the library in real-world conditions and experiment on synthetic and (benchmark) medical data, analyzing the benefits and the costs compared to a centralized approach.
• Publish open source code and integrate in existing libraries
• Publish scientific results in medical and computer science conferences

The Declearn project is available at https://gitlab.inria.fr/magnet/declearn/declearn2

• Implement federated and privacy-preserving algorithms for machine learning
• Identify appropriate multicentric medical studies that fit the decentralized setting
• Experiment with medical partners
• Evaluation of results
• Reporting, disseminating and presenting results

• Programming skills, preferably in Python, C/C++, R, Bash scripting
• Interest in machine learning and medical applications. Good understanding of scientific papers on machine learning
• Good communication skills; communication and animation of software development communities, git workflow

• Subsidized meals
• 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
• Access to vocational training
• Social security coverage

According to profile