- 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
- 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)
- Mastering English is essential
- 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
Engineer - Scientific programmer in privacy-preserving federate learning with applications in oncology - Villeneuve-d'Ascq, France - INRIA
Description
Contexte et atouts du poste
This engineer position will be supported by the 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.
Mission confiée
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
Principales activités
Compétences
Technical skills and level required :
Languages :
Relational skills :
Avantages
Rémunération
According to the profile