Temporary scientific engineer / A platform for synthetic data generation and privacy evaluation / PhD or equivalent

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 CAPS'UL project. The position will be based in the MAGNET team in Lille, in very close collaboration with the Lille Hospital.

The CAPS'UL project objective is to promote digital health culture for current and future healthcare professionals. Part of the project concerns the design of a high-performance tool for practical situations, enabling concrete and effective collaboration between the various training, socio-economic and medico-social players in the implementation of training courses. It will provide a credible immersive environment (real software and simulated healthcare data) and teaching scenarios for the entire teaching community.

The INRIA MAGNET team (and hence the recruited collaborators) will contribute to this project by researching machine learning algorithms for synthetic data generation with privacy constraints and dedicated to training.

The recruited engineer will collaborate with colleagues in the MAGNET team and the CAPS'UL project consortium. In particular, the work will contribute to build a platform for synthetic (longitudinal) data generation together with a toolbox for privacy auditing. The main originality lies in the definition of data utility as it should reflect the variety of situations that can help professionals in a training context.

All developed software will be open-source.

The principal activities will be in two steps.

• First period
  • Select and gather state of the art implementations of machine learning methods for synthetic data generation
  • Extract and prepare health data for training machine models
  • Generate synthetic data and evaluate privacy with state of the art attacks
  • Test algorithms and run experiments
• Second period
  • Design and prototyping of key algorithms
  • Create appropriate tests and documentation
  • Integrate such implementations in the CAPS'UL platform
  • Test algorithms and run experiments

• Strong background in Computer Science
• Strong programming skills in Python
• Background in privacy and machine learning
• Prior experience in the health domain will be an asset

• 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