Post-Doctoral Research Visit F - M Uncertainty-Aware Causal Graph Learning For Interpretable Biomedical Discovery H/F

INRIA

Post-Doctoral Research Visit F/M Uncertainty-Aware Causal Graph Learning for Interpretable Biomedical Discovery
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

Niveau d'expérience souhaité : Jusqu'à 3 ans

A propos du centre ou de la direction fonctionnelle

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part. With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation. Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services. It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more. The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

Contexte et atouts du poste

About the centre

The successful candidate will integrate the EPIONE (e-patient for e-medicine) research project at the Inria Center at Université Côte d'Azur (UniCA, Sophia Antipolis, France). The longstanding research activity of the Epione Research Group revolves around the analysis, modeling, and treatment of biomedical data, with a focus on machine learning, medical imaging, computational anatomy, and computational physiology.

The postdoctoral fellow is expected to spend a significant part of the fellowship at University College London (UCL, London, UK), hosted in the Statistical Science Department. The Department is the oldest department of statistics in the world, hosting a rich research environment spanning from biostatistics and causal discovery to economics and machine learning.

During the postdoctoral fellowship, the candidate will have the opportunity to interact with researchers and students from the EPIONE team and participate in the scientific life of the team and of the Inria UniCA center. In addition, the postdoctoral fellow will benefit from the interaction with the Causality and the Computational Statistics and Machine Learning research themes at UCL Stats.

Context

Every year Inria International Relations Department has a few postdoctoral positions in order to support Inria international collaborations.

The postdoctoral contract will have a duration of 12 to 24 months. The default start date is November 1st, 2026 and not later than January, 1st 2027. The postdoctoral fellow will be recruited by one of the in France but it is recommended that the time is shared between France and the partner's country (please note that the postdoctoral fellow has to start his/her contract being in France and that the visits have to respect Inria rules for missions)

This postdoctoral project will be embedded in the Inria-London Program (United Kingdom). In 2025, Inria and UCL (University College London) signed an agreement to establish the Inria-UCL Joint Centre, within the Inria-London Programme, which serves both as Inria's physical hub in London and as a scientific collaboration programme, with the aim of strengthening and structuring the impact of scientific collaborations between the institute and its London-based partners - foremost among them UCL - focusing on artificial intelligence in the broadest sense (including robotics, machine learning, computer vision and personalized medicine) and beyond to other areas such as quantum computing and cybersecurity, for example. The priority for the Inria London Program is given this year to a postdoctoral position between UCL and Inria in the frame of the Joint Centre.

Within this context, the successful candidate will be integrated into the Inria-UCL associate team Graph-Cure (), whose overall scope is the development of causal and graph-based modeling for AI-enhanced healthcare, with a focus on asthma.

Mission confiée

Candidates for postdoctoral positions are recruited after the completion of their Ph.D. or a first postdoctoral period. To be eligible, candidates must have defended their Ph.D. no more than 3 years before the start date of the contract. As the start date will be between November 1, 2026 and January 1, 2027, the latest eligible Ph.D. defense date will vary accordingly (approximately between November 1, 2023 and January 1, 2024).

In order to encourage mobility, the postdoctoral position must take place in a scientific environment that is truly different from the one of the Ph.D. (and, if applicable, from the position held since the Ph.D.); particular attention is thus paid to French or international candidates who obtained their doctorate abroad.

Mission

This postdoctoral position is part of the Inria UniCA-UCL associate team Graph-Cure, which aims to combine statistical methods for sets of graphs with robust and reliable causal graph learning, with applications to healthcare. The PDRA will contribute to two complementary research directions:

1) Uncertainty-aware causal discovery. The candidate will develop and evaluate ensembling approaches for causal graph estimation, integrating both parametric and non-parametric methods to produce robust and reliable causal conclusions under uncertainty.

2) Dimensionality reduction for causal graphs. The candidate will design and compare dimensionality reduction procedures tailored to causal graphs learned in high-dimensional settings, with a strong emphasis on interpretability.

These methodological contributions will be validated on a clinically motivated application: the analysis of single-cell gene expression data from lung tissue collected with and without steroid exposure, with the aim of tracing both therapeutic and adverse effects of steroids in severe asthma patients.

The PDRA will be jointly supervised by Dr. Irene Balelli (Inria UniCA), whose research focuses on causal discovery and disentanglement with applications to computational biomedicine, and Dr. Anna Calissano (UCL, Department of Statistics), whose research focuses on statistical methods for sets of graphs. Prof. Karla Diaz-Ordaz (UCL, Department of Statistical Science), whose primary research area is causal inference and the development of machine-learning-based estimators for causal quantities, will also be involved as a collaborator within the Graph-Cure team. Dr. Rocio Martinez-Nunez (Guy's Hospital, KCL), a Reader in RNA Biology and Immunity with expertise in post-transcriptional regulation in asthma and respiratory disease, will provide the clinical application context and data.

Collaboration

The proposed position builds on an active and growing collaboration between Dr. Balelli and Dr. Calissano, established in 2025. This collaboration has already secured EPSRC funding through the CHAI Hub (project CausalGene), which has supported a postdoctoral researcher in the UK (H. Araujo, January 2026 - June 2026) working on preliminary analyses of Dr. Martinez-Nunez's data. The two investigators also jointly co-supervised a master's student (A. Lang), who is now pursuing a PhD at Inria UniCA on related topics. The Graph-Cure associate team, for which Drs. Balelli and Calissano serve as principal investigators, was formally established in March 2026 to provide the institutional framework for this joint research programme. The Inria-DRI PDRA position would further consolidate this partnership, sustain its scientific momentum, and deepen institutional ties between Inria UniCA and UCL.

Instructions to apply:

You can directly send your application to:

- Irene Balelli: ****@****.**
- Anna Calissano: ****@****.**

Deadline for application: June 7, 2026

Principales activités
- Conduct a structured review of existing methods for causal graph estimation, uncertainty quantification, and dimensionality reduction in high-dimensional settings.
- Develop novel ensembling strategies for causal discovery that integrate parametric and non-parametric approaches, with principled uncertainty quantification around estimated causal structures.
- Design and benchmark dimensionality reduction techniques applied at both the dataset and graph levels, with a focus on interpretability of the resulting causal representations.
- Apply developed methods to single-cell gene expression data from asthma patients, in collaboration with Dr. Martinez-Nunez, to investigate the causal effects of steroid treatment on lung cell gene expression profiles.
- Disseminate results through publications in high-quality international venues and presentations at relevant conferences.
- Contribute to the scientific life of the Graph-Cure associate team, including joint meetings with UCL and KCL partners and potential co-supervision of junior researchers.

Compétences

Required

- PhD in statistics, mathematics, machine learning, or a closely related field.
- Strong background in at least one of the following: probabilistic modelling, causal inference, graph-based statistical methods.
- Proficiency in scientific programming (e.g. Python, R) and familiarity with relevant libraries for statistical computing and machine learning.
- Ability to work independently and conduct rigorous research at the interface of methodology and application.
- Strong written and oral communication skills in English.

Desirable

- Experience with causal learning.
- Familiarity with dimensionality reduction techniques for structured or non-Euclidean data.
- Knowledge of single-cell genomics data and associated computational workflows.
- Experience working in an interdisciplinary or international collaborative setting.

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
- Contribution to mutual insurance (subject to conditions)

Rémunération

Gross Salary: 2788 € per month