2021-04080 - Engineer in transparent privacy preserving AI: privacy assessment and reasoning
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Ingénieur scientifique contractuel

Niveau d'expérience souhaité : Jusqu'à 3 ans

A propos du centre ou de la direction fonctionnelle

The Inria Lille - Nord Europe Research Centre was founded in 2008 and employs a staff of 320, including 280 scientists working in fourteen research teams. Recognised for its outstanding contribution to the socio-economic development of the Hauts-De-France région, the Inria Lille - Nord Europe Research Centre undertakes research in the field of computer science in collaboration with a range of academic, institutional and industrial partners.

 The strategy of the Centre is to develop an internationally renowned centre of excellence with a significant impact on the City of Lille and its surrounding area. It works to achieve this by pursuing a range of ambitious research projects in such fields of computer science as the intelligence of data and adaptive software systems. Building on the synergies between research and industry, Inria is a major contributor to skills and technology transfer in the field of computer science.

Contexte et atouts du poste

This engineer position will be supported by the TIP project on Transparent artificial Intellgence preserving Privacy. This project will be jointly funded by I-Site, INRIA, U-Lille and MEL. This is a project in the MAGNET team (INRIA-Lille, https://team.inria.fr/magnet/).

While this position will be in the MAGNET team in Lille, we will collaborate with users (e.g. medical research groups in CHU-Lille) for the validation and exploitation of the work.

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. Explainable machine learning aims at learning models which are not only accurate but also can be explained to humans.
The overall goal of the TIP project is to develop, exploit and explain a sound understanding of privacy-preserving strategies in larger AI-based processes involving massive numbers of agents among whom a part may be malicious.
Key challenges are related to the fact that we study applications from a holistic point of view (rather than individual operations in isolation), the need for transparency and explainability, and the need to consider more realistic assumptions than the popular honest-but-curious model.

To realize this project, a team of PhD students, post-docs, senior researchers and engineers will collaborate to perform the necessary research and develop a prototype. The successful candidate will be a member of this team. The TIP project team will collaborate with other members of the MAGNET group, e.g. engineers will collaborate with the ADT-Tailed project.

More project information will be posted at http://researchers.lille.inria.fr/jramon/projects/tip.html

Mission confiée

The recruited engineers will collaborate with the TIP project researchers and the MAGNET team engineers and will develop parts of the project's open source library, which will also serve as a proof of concept of the research results. 

We aim to recruit several engineers on the TIP project.  This vacancy focuses on inference techniques aimed at privacy assessment and reasoning about information flows.

Principales activités

  • Studying new algorithms for reasoning about data privacy
  • Design and prototyping of key algorithms, and include these in the TAILED open source project.
  • Collaborating with researchers to test algorithms and run experiments



Technical skills and level required :

  • a strong understanding of algorithms
  • software design and development skills (relevant code may include Python and/or C/C++)
  • understanding of process models and (probabilistic) reasoning techniques

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 (after 6 months of employment) 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