PhD Position F/M PhD student on federate learning and multi-party computation techniques for prostate cancer

Contract type : Fixed-term contract

Level of qualifications required : Graduate degree or equivalent

Fonction : PhD Position

Level of experience : Up to 3 years

About the research centre or Inria department

The Inria Lille - Nord Europe Research Centre was founded in 2008 and employs a staff of 360, including 300 scientists working in fifteen 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.


This PhD student position will be supported by the HE Flute project

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 goal of the multi-disciplinary FLUTE project is to advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments.

The INRIA MAGNET team (and hence the recruited collaborators) will contribute to this project among others by researching machine learning algorithms and multi-party protocols with improved scalability in the context of medical data, e.g., by exploiting data sparsity.   This research will involve both theoretical and more applied components.  As coordinator INRIA will also contribute to the integration of the software developed in the FLUTE project (and the complementary TRUMPET project).



The recruited PhD student will collaborate with colleagues in the MAGNET team and the FLUTE project consortium in general.  Part of the work may involve travel to other partners.

If the research features a prototype, it will contribute to the project's open source library and may be supported by engineers in the team.

Possible domains of research include (but are not limited to):

  • Cryptography-based strategies to improve the security of privacy-preserving AI systems.
  • Inference methods for privacy assessment
  • Design and development of the FLUTE platform and its supporting algorithms


Main activities

  • Contribute to the research of the FLUTE project
  • Collaborate with other MAGNET and FLUTE team members
  • Disseminate research results



The ideal candidate will have the following skills:

  • Good mastery of English
  • A strong background in computer science and mathematics.  Subject matters which will be needed during the research include (but are not limited to) machine learning, statistics, cryptography, distributed systems, constraint programming.
  • Good programming skills (e.g., C/C++ or python) and supporting tools.
  • Relational skills, e.g., working in a team, effective reporting and communication with all involved stakeholders.

Benefits package

  • 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


1st and 2nd year : 2051€ gross monthly salary (before taxes) 

3rd year : 2158€ gross monthly salary (before taxes)