Post-Doctoral Research Visit F/M Post-doc Position on Fairness and Privacy in Trustworthy machine Learning (F/M)

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Offre n°2026-10660

Post-Doctoral Research Visit F/M Post-doc Position on Fairness and Privacy in Trustworthy machine Learning (F/M)

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Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

A propos du centre ou de la direction fonctionnelle

Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region.

For over 10 years, the Inria center at the University of Lille has been 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).

Contexte et atouts du poste

The selected candidate will be based in Lille in the MAGNET team. The main objective of the team is to develop ethically acceptable machine learning algorithms focusing on fairness, privacy, and decentralized learning and to empower end users of artificial intelligence. The candidate will be under the supervision of Michaël Perrot.

Mission confiée

Machine learning is nowadays used in various applications, such as medical diagnosis and speech recognition. Its success stems from the performance of learned models, sometimes reaching human-level capabilities. However, deploying these models on a larger scale requires more than just accuracy, and it is imperative to consider fairness and privacy when human lives are affected. For instance, a model used for medical diagnosis or an automated hiring process should not be biased against subgroups of the population. Similarly, released models should not leak information about the data that was used to train them. Several approaches have been proposed in the literature to enforce fairness and privacy (either individually or jointly) in machine learning models.

The goal of this post-doc position will be to make fairness and privacy preserving approaches developed in the team more accessible to the machine learning community. On the one hand, the FairGrad library was developed a few years ago as an easy to use approach to enforce fairness. A first objective will thus be to expand it, benchmark it, and integrate it to existing fairness libraries (such as AIF360 and Fairlearn) as well as general purpose machine learning libraries (such as scikit-learn). On the other hand, recent work in the team showed that it is possible to learn from locally private data with a controlled bias [1]. A second goal will thus be to propose an efficient implementation of the proposed approach and to benchmark it on a variety of tasks. Finally, a third objective will be to integrate both methods with each other to propose a well founded pipeline to learn privacy preserving and fair machine learning models.

[1] Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent, Jean Dufraiche, Paul Mangold, Michael Perrot, Marc Tommasi, 2026.

Principales activités

Review and follow the existing libraries on Fair and Privacy preserving Machine Learning.

Maintain, benchmark and expand the scope of the FairGrad library.

Implement, maintain, and benchmark a machine learning pipeline for machine learning from locally private data.

Compétences

A good candidate will have the following skills:

A good command of English

A good command of Python

A strong background in implementation and experimentation

A strong background in mathematics

A good knowledge of machine learning, statistics and algorithms

Some knowledge on fairness and/or privacy would be a plus

Please follow the instructions given in https://team.inria.fr/magnet/how-to-apply/ to set up your application file.

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

2788 € grossly salary per month

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Informations générales

Thème/Domaine : Optimisation, apprentissage et méthodes statistiques

Statistiques (Big data) (BAP E)

Ville : Villeneuve d'Ascq

Centre Inria : Centre Inria de l'Université de Lille

Date de prise de fonction souhaitée : 2027-03-01

Durée de contrat : 1 an, 6 mois

Date limite pour postuler : 2026-11-27

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.

Consignes pour postuler

Sécurité défense :

Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

Politique de recrutement :

Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

Contacts

Équipe Inria : MAGNET

Recruteur :

Perrot Michael /

L'essentiel pour réussir

A successful candidate will

Collaborate in the team and where applicable with external researchers and engineers

Organize work efficiently and make a good balance between the several priorities

Report regularly

A propos d'Inria

Inria, l'institut national de recherche dans les sciences et technologies du numérique, est en appui de l’État pour les stratégies nationales de recherche et d’innovation du numérique en tant qu'Agence de programmes. Inria mène plus de 300 projets de recherche et d’innovation avec ses 3500 scientifiques, ingénieurs et personnels d’appui, en partenariat avec les universités et l’écosystème numérique (entreprises, entrepreneurs, acteurs publics). Ensemble, nous explorons des domaines clés comme l'intelligence artificielle, la cybersécurité, l’informatique quantique, le Cloud, la transformation numérique de la santé, les jumeaux numériques ou encore les technologies numériques pour la défense. Nous construisons des solutions concrètes telles que des logiciels, des startups technologiques, des partenariats avec les entreprises du tissu national et des formations de pointe. Notre objectif : l’impact scientifique, technologique et industriel au service de la souveraineté numérique de la France.

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