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Engineer - Scientific programmer in privacy-preserving federated machine learning (F/M)INRIA • Villeneuve d'Ascq, FR
Engineer - Scientific programmer in privacy-preserving federated machine learning (F/M)

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

INRIA • Villeneuve d'Ascq, FR
Il y a 17 jours
Type de contrat
  • Temps plein
Description de poste

Contexte et atouts du poste

This engineer position will be supported by the

Mission confiée

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

Principales activités

  • 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.

Compétences

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

Avantages

  • 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

Rémunération

According to profile

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Engineer - Scientific programmer in privacy-preserving federated machine learning (F/M) • Villeneuve d'Ascq, FR

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