Research Internship M2 - Stage M2 Building Efficient Generative Models For Weather Forecasting H/F - 75

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.Research Internship M2 / Stage M2: Building efficient generative models for weather forecasting
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : Stage

Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Stagiaire de la recherche

Contexte et atouts du poste

This master's thesis will be supervised by Emmanuel De Bézenac (ARCHES team, Inria Paris).

Mission confiée

Recent advances in generative modeling have opened new possibilities for representing complex geophysical fields. In weather forecasting, modern diffusion, flow-matching, and flow-based models have demonstrated strong performance, but current approaches struggle with multiscale atmospheric structure and with the fine-to-coarse interactions typical of physical fields. Improving the spectral and dynamical fidelity of these generative models is an important step toward reliable data-driven forecasting systems.

The master thesis will investigate how to adapt and refine generative modeling frameworks-particularly diffusion and flow-matching methods-to atmospheric data. The focus is on understanding how different scales of motion are represented during the generation process, how multiscale structures propagate through the model dynamics, and how model design choices influence numerical stability and physical realism. The work will contribute to the development of next-generation AI weather models capable of representing both global-scale patterns and fine-scale variability.

Principales activités

- Study existing generative modeling frameworks used in scientific data (diffusion, flow matching, deterministic flows) and analyse how they behave on multiscale atmospheric fields.
- Explore strategies for representing scale interactions during the generation process, such as alternative noise designs, interpolation schemes, or multiscale parameterizations.
- Develop and test model adaptations tailored to weather data-e.g., architecture choices, training procedures, or sampling methods that improve spectral accuracy and physical consistency.
- Contribute to prototype implementations and small-scale experiments within the ARCHES weather modeling effort.

Compétences

Technical skills and level required :

- Bachelors and Master's degrees in Computer Science (Informatique), statistics, mathematics, or a related field
- Machine learning, data mining, statistics, and/or AI coursework and/or projects
- Familiarity with modern machine learning / deep learning software, tools, pipelines

Languages :

- Written competency in English
- Oral competency in English or French

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