Job offer

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

Specific Requirements

We are looking for a candidate with a strong background in computer science, with interest in research (including the mathematics needed to realize privacy) who welcomes the broad range of challenges leading to a successful result.

The development to which the engineers will contribute will include among others parts requiring (a) highly efficient mathematical code (for the reasoning components), (b) communication and security related modules, (c) interaction with AI libraries (e.g., scikit learn) and (d) analyzing and transforming algorithms and queries provided as input by the ML user. Being familiar with at least one of these areas of software development is an important asset.

It is important to integrate in the academic setting where everybody is continuously learning, the team where collaboration is important and the project with its specific goals and partners.

Languages FRENCH

• 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 Selection process