Engineer - Scientific programmer in privacy-preserving federated machine learning (F/M)

Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region.

For over 10 years, the Inria center at the University of Lille has been 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 EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT).

This engineer position will be supported by the  PEPR IA Redeem project.   While this position will be in the MAGNET team in Lille, we will collaborate with the several European project partners.

While AI techniques are becoming ever more powerful, there is a growing concern about potential risks and abuses. As a result, there has been an increasing interest in research directions such as privacy-preserving machine learning, explainable machine learning, fairness and data protection legislation.
Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data is not revealed. Notions such as (local) differential privacy and its generalizations allow to bound the amount of information revealed.

The MAGNET team is involved inthe related TRUMPET, FLUTE and REDEEM projects, and is looking for team members who can in close collaboration with other team members and national & international partners contribute to one or more of these projects.  All of these projects aim at researching and prototyping algoirhtms for secure, privacy-preserving federated learning in settings with potentially malicious participants.  The TRUMPET and FLUTE projects focus on applications in the field of oncology, while the REDEEM project has no a priori fixed application domain.

The recruited engineer will collaborate with colleagues in the MAGNET team and the REDEEM project.  In particular, the work will contribute to REDEEM's open source library, by collaboratively designing and developing the overall architecture and contributing modules providing privacy enhancing technologies (PETs) and privacy assessment functionality based on MAGNET scientific advances

By default all developed software will be open-source.

Tasks may include

• developing algorithms, e.g., cryptographic or statistical modules, modules supporting the knowledge discovery pipeline and its automatisation
• testing algorithms through systematic benchmarking / experimentation
• applying algorithms in applications

• Studying new algorithms for reasoning about data privacy
• Automatically analyzing and transforming algorithms and queries provided as input.
• Design and prototyping of key algorithms
• Create appropriate documentation
• Integrate such implementations in the FLUTE platform
• test algorithms and run experiments
• Prepare further research and development starting from the FLUTE platform.

Technical skills and level required :

• a strong understanding of distributed algorithms
• software design and development skills (relevant code may include Python and/or C/C++)
• understanding of process models and (probabilistic) reasoning techniques
• understanding of programming language internals (e.g., abstract syntax trees)

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

• 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