Phd Position F - M Fairness In Learning-Augmented Online Algorithms H/F - 95

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.PhD Position F/M Fairness in learning-augmented online algorithms
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Doctorant

A propos du centre ou de la direction fonctionnelle

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .

The centre has 40, 27 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.

Contexte et atouts du poste

The recruited engineer will work in the FairPlay team. FairPlay is a joint team between Inria, ENSAE and Criteo with around 8 permanent researchers and 15 PhD students/postdocs from all three partners institution. The team is bi-localized,hosting atCREST (on the Polytechnique campus) but working from Criteo HQ in Paris once a week.

Mission confiée

Learning-augmented online algorithms use machine-learning-based predictions to help
make decisions in online algorithms where the decisions such as selection or matching must be
made sequentially. They appear in several real-world scenarios, such as job assignments and online
advertising. They have received attention in recent years from the perspective of pure performance.
In this PhD, we propose to explore fairness questions that arise from these problems in cases where
decisions are made about individuals and should be made free of discrimination. We study in
particular how unfairness in predictions can affect the final outcome in algorithms with prediction,
with the final objective of deriving algorithms with fairness guarantees. Key challenges include
fairness constraints in prophet inequalities, where selection must balance quality and group fairness,
the impact of biased predictions on online selection and bipartite matching and the the development
of fair decision-making algorithms. Further work will explore correcting the bias of the prediction
at decision time instead of correcting the prediction. The objective of this work is to contribute to
the design of more equitable online algorithms with predictions.

Principales activités

Main activities: conduct research on fairness in online algorithms

Additional activities: document the results through publications and talks

Compétences

Technical skills and level required : A strong background in mathematics, in particular in probability and online learning.

Languages : French or English

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 (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

Rémunération

Monthly gross salary : 2.300 euros