Post-Doctoral Research Visit F/M [POSTDOC2024-PREMEDICAL] Broadening the use of bilevel optimization in machine learning

Contract type : Fixed-term contract

Renewable contract : Yes

Level of qualifications required : PhD or equivalent

Fonction : Post-Doctoral Research Visit

About the research centre or Inria department

The Inria centre at Université Côte d'Azur includes 37 research teams and 8 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.



The hired postdoc will join PreMeDICaL, an Inria/Inserm research team based in Montpellier
(France), and will be jointly supervised by Aurélien Bellet and Julie Josse. PreMeDICaL specializes
in developing precision medicine methods through causal learning and federated learning, while
ensuring the confidentiality of medical data. The team is composed of researchers in statistics,
machine learning, AI, as well as clinicians, and aims to bridge the gap between fundamental research
and its effective use in healthcare. Through his/her research, the hired postdoc will contribute to
the research axes "Personalized medicine by integration of different data sources" and "Personalized
medicine with privacy and fairness guarantees".


Bilevel optimization has recently witnessed significant advancements with the development
of efficient general-purpose algorithms with proven convergence properties. This postdoc aims
to explore how these techniques can offer novel and provably effective solutions to challenging
machine learning tasks that can be framed as bilevel optimization problems. Emphasis will be
placed on leveraging these methodologies for privacy-preserving and fair machine learning, as
well as for learning on data with missing values.

See detailed topic here:

Main activities



The applicant must hold a PhD in machine learning, optimization or related fields. She/he is
expected to have strong mathematical skills (e.g., numerical optimization, probability, statistics,
linear algebra). Some knowledge in bilevel optimization, trustworthy machine learning and/or
missing values is a plus.

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
  • Contribution to mutual insurance (subject to conditions)


Duration: 18 months
Location: Sophia Antipolis, France
Gross Salary per month: 2788€ brut per month