Software engineer private and decentralized machine learning

Software engineer on private and decentralized machine learning (H/F)

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.

For more than 10 years, the Inria University of Lille centre has been located at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on Avenue de Bretagne in Lille, on the Eura Technologies site of economic excellence dedicated to information and communication technologies (ICT)

The position will be supported by Fed Malin, a collaborative project on Federated Learning between 11 teams  project addresses FL challenges when deployed over the internet (privacy, heterogeneity, energy, fairness, ...) and has medicine as a main targeted application domain. Fed Malin develops several software tools, including the open source library Dec Learn ((Use the "Apply for this Job" box below).) for private and decentralized/federated machine learning and data analysis.

The hired engineer will contribute to the ongoing development of Dec Learn, expanding its capabilities with new algorithms and enhanced functionalities.

The activities will include interactions with the members of the project, the Magnet and Premedical teams (researchers and engineers). We also expect to conduct multi-centric medical studies across several hospitals. 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
• 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 /declearn2

• Implement federated and privacy-preserving algorithms for machine learning
• Experiment with medical partners on multicentric medical studies
• Evaluation of results
• Reporting, disseminating and presenting results
• Programming skills in Python, including object oriented programming, unit testing, documentation writing, deployment tools, asynchronous programming and networking.
• Good understanding of scientific papers on machine learning.
• Interest for machine learning and medical applications.
• Good communication skills; communication and animation of software development communities, git workflow

• 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