PhD (CIFRE) with AXA and INRIA - AI Weather Models for Extreme Events Risk Estimation

AXA Group; Paris; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

AXA Group, en partenariat avec INRIA, propose un doctorat CIFRE à Paris axé sur l'évaluation et le développement de modèles météo IA pour estimer les risques liés aux événements extrêmes. Le candidat travaillera sur des projets innovants qui allient science des données et prévisions climatiques, contribuant à des publications et à des applications réelles.

Prestations

Accès à un réseau de chercheurs à l'international

Contributions reconnues à des publications

Environnement de travail dynamique et inclusif

Qualifications

• Expérience en programmation Python et en bibliothèques d'apprentissage profond.
• Connaissance avancée en apprentissage automatique et en modélisation statistique.
• Niveau d'anglais avancé requis.

Responsabilités

• Développer des modèles météo IA pour simuler des événements extrêmes.
• Produire des articles de recherche pour des conférences ML de haut niveau.
• Explorer des solutions pour améliorer les modèles existants ou créer de nouvelles approches.

Connaissances

Programmation en Python

Deep Learning

Statistical Modelling

Formation

MSc en informatique, AI, data science, ou équivalent

Description du poste

Social network you want to login/join with:

PhD (CIFRE) with AXA and INRIA - AI Weather Models for Extreme Events Risk Estimation, Paris

col-narrow-left

Client:

AXA Group

Location:

Paris, France

Job Category:

Other

-

EU work permit required:

Yes

col-narrow-right

Job Reference:

31ca0149d84f

Job Views:

3

Posted:

24.06.2025

Expiry Date:

08.08.2025

col-wide

Job Description:

Recent advancements in AI weather models have shown promising capabilities in weather prediction from short to medium-term (0 to 10 days lead time), comparable to physic-based state-of-the-art models. Notable examples include FourCastNet, SFNO, GraphCast, GenCast, Pangu-Weather, which have adopted a trend observed in other fields (, language models, vision models, multi-modal models): they leverage large architectures with an extensive pre-training on very large datasets. This type of large models (also called foundation models) facilitates the development of downstream applications.

In the context of weather forecasting, these AI models are typically trained on the gold standard global reanalysis dataset ERA-5, developed by the European Centre for Medium-Range Weather Forecasts. ERA-5 is widely used for weather and climate studies due to its high resolution, comprehensive coverage, and accurate representation of atmospheric conditions from 1979 to the present day. AI models, such as FourCastNet or GraphCast, are trained on ERA5 data: they take a snapshot of the atmosphere state at time (t) and they are trained to predict the atmospheric conditions at time (t+1) (typically 6 hours later).

This PhD aims to assess and develop the capabilities of AI weather models in simulating extreme events, a promising avenue for risk estimation and risk prevention. However, potential issues may prevent accurate simulations: for instance, model biases towards average weather conditions (, reliance on RMSE metric) and the underrepresentation of extreme events in the ERA5 dataset.

The PhD (under a CIFRE scheme) will be hosted by the AI research team at AXA Group Operations in collaboration with the Natural Catastrophe R&D modelling team at AXA and the ARCHES team at INRIA. The primary objective is to develop knowledge and methodologies to better understand climate risks using AI weather models. The PhD student will evaluate and enhance the capabilities of AI weather models to simulate plausible and unobserved extreme events, focusing on identifying or developing datasets and metrics to assess model performances. The PhD student will explore solutions to improve existing models or create new approaches to address the limitations in extreme event simulation. The downscaling of the simulations to a higher resolution than the native ERA-5 resolution will also be addressed during the PhD. A key component of this work will involve estimating climate risks, particularly to determine the optimal set of initial conditions to input AI weather models to get, in fine, an accurate estimation of the risk of extreme weather event across various geographical locations. While this research is open to any major climate risks (tropical cyclones, extra-tropical cyclones, windstorms, hail, floods, heatwaves, etc.), the student may focus on a subset of perils.

Expected Contributions

During the thesis, the PhD student is expected to produce research articles to be submitted to high-quality peer-reviewed ML workshops, conferences and journals ( ICML, IJCAI, NeurIPS, JMLR...). Algorithmic implementations of the conceived methodology will be made available through libraries. The algorithms and methodologies developed during the PhD will be applied to real-world usecases.

The PhD (under a CIFRE scheme) will be jointly hosted by the AI research team at AXA Group Operations in collaboration with the Natural Catastrophe R&D modelling team at AXA and the ARCHES team at INRIA.

The PhD student will also benefit from interactions with other researchers from AXA AI Research ecosystem, in particular the AXA-Sorbonne University Joint Research Lab (TRAIL), EPFL campus in Lausanne (Switzerland) and the Stanford University campus in Palo Alto (US).

Recent advancements in AI weather models have shown promising capabilities in weather prediction from short to medium-term (0 to 10 days lead time), comparable to physic-based state-of-the-art models. Notable examples include FourCastNet, SFNO, GraphCast, GenCast, Pangu-Weather, which have adopted a trend observed in other fields (, language models, vision models, multi-modal models): they leverage large architectures with an extensive pre-training on very large datasets. This type of large models (also called foundation models) facilitates the development of downstream applications.

In the context of weather forecasting, these AI models are typically trained on the gold standard global reanalysis dataset ERA-5, developed by the European Centre for Medium-Range Weather Forecasts. ERA-5 is widely used for weather and climate studies due to its high resolution, comprehensive coverage, and accurate representation of atmospheric conditions from 1979 to the present day. AI models, such as FourCastNet or GraphCast, are trained on ERA5 data: they take a snapshot of the atmosphere state at time (t) and they are trained to predict the atmospheric conditions at time (t+1) (typically 6 hours later).

This PhD aims to assess and develop the capabilities of AI weather models in simulating extreme events, a promising avenue for risk estimation and risk prevention. However, potential issues may prevent accurate simulations: for instance, model biases towards average weather conditions (, reliance on RMSE metric) and the underrepresentation of extreme events in the ERA5 dataset.

The PhD (under a CIFRE scheme) will be hosted by the AI research team at AXA Group Operations in collaboration with the Natural Catastrophe R&D modelling team at AXA and the ARCHES team at INRIA. The primary objective is to develop knowledge and methodologies to better understand climate risks using AI weather models. The PhD student will evaluate and enhance the capabilities of AI weather models to simulate plausible and unobserved extreme events, focusing on identifying or developing datasets and metrics to assess model performances. The PhD student will explore solutions to improve existing models or create new approaches to address the limitations in extreme event simulation. The downscaling of the simulations to a higher resolution than the native ERA-5 resolution will also be addressed during the PhD. A key component of this work will involve estimating climate risks, particularly to determine the optimal set of initial conditions to input AI weather models to get, in fine, an accurate estimation of the risk of extreme weather event across various geographical locations. While this research is open to any major climate risks (tropical cyclones, extra-tropical cyclones, windstorms, hail, floods, heatwaves, etc.), the student may focus on a subset of perils.

Expected Contributions

During the thesis, the PhD student is expected to produce research articles to be submitted to high-quality peer-reviewed ML workshops, conferences and journals ( ICML, IJCAI, NeurIPS, JMLR...). Algorithmic implementations of the conceived methodology will be made available through libraries. The algorithms and methodologies developed during the PhD will be applied to real-world usecases.

Working Environment

The PhD (under a CIFRE scheme) will be jointly hosted by the AI research team at AXA Group Operations in collaboration with the Natural Catastrophe R&D modelling team at AXA and the ARCHES team at INRIA.

The PhD student will also benefit from interactions with other researchers from AXA AI Research ecosystem, in particular the AXA-Sorbonne University Joint Research Lab (TRAIL), EPFL campus in Lausanne (Switzerland) and the Stanford University campus in Palo Alto (US).

Your Profile

Required:

·MSc. in computer science, AI, data science, applied mathematics, statistics or equivalent.

·Good experience in programming in python and deep learning libraries ( pytorch...)

·Very good knowledge in deep learning, machine learning and statistical modelling.

·Advanced level in English (technical discussions, presentations and paper writing are expected)

Preferred:

·Education, research, experience, projects in climate sciences, extreme events, risk estimation.

·Previous research experience: research projects, internships, publications...

About AXA

As a world-leading insurance company, we act for human progress by protecting what matters. With 153,000 employees in 54 countries working with 105 million customers, we’ve created a truly dynamic and vibrant community. Inclusion and diversity link closely with our values, and together we’re nurturing a culture of
respect, for each other, for our customers and the communities around us. Join AXA and you’ll feel like you belong, are included and can thrive. You’ll be able to shape the way you work and truly grow your potential as you seek out new opportunities, push boundaries and benefit people in critical moments of their lives. This is your chance to build the tomorrow you want. Know you can.

About the Entity

AXA is becoming a sustainable tech-led company and at AXA Group Operations we are one of the major catalysts for this transformation.

We set the tone by triggering and empowering the evolution of our insurance business model through technology and innovation, driving its concrete implementation globally at speed, with a high quality of advisory and execution.

We are present across 17 countries with committed, highly qualified teams. We leverage technology, data, sourcing, security and investment allocation in a global way, but also achieve economies of scale and synergies when necessary.

At AXA Group Operations, we want to be recognized in three fields of action:

State-of-the-art Data Technology to drive customer experience

State-of-the-art Procurement & Sourcing to drive efficiency and better manage risks

High-Performing Global Team for stronger partnerships with AXA entities

What We Offer

We bring together the expertise, cultural diversity and creativity of over 8,000 employees worldwide and we’re committed to equal opportunities in all aspects of employment (gender, LGBT+, disabled persons, or people of different origins) and to promoting Diversity &Inclusion by creating a work environment where all employees are treated with dignity and respect, and where individual differences are valued.