Engineer - Scientific programmer in privacy-preserving federate learning with applications in oncology

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 EuraTechnologies site of economic excellence  dedicated to information and communication technologies (ICT).

This engineer position will be supported by the HE Trumpet project, the HE Flute project and/or 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 TRUMPET/FLUTE/REDEEM projects' consortia.  In particular, the work will contribute to TRUMPET/FLUTE's platform and 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 medical applications, e.g., TRUMPET's lung cancer or head&neck cancer or FLUTE's prostate cancer use cases.

• 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

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

• 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 the profile