Publiée 23 juillet 2026
Research engineer F/M Stochastic modelling, mathematical analysis and numerical simulation of oceanic flow, small-scale dynamics, wave-current interaction
Inria
Rennes, Hauts-de-France 60420, France
CDI
A propos du centre ou de la direction fonctionnelle
The Inria Centre at Rennes University is one of Inria's nine centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Contexte et atouts du poste
The Odyssey team is offering a 12 month position on ocean modelling within the ERC Stuod (Stochastic transport in ocean dynamics). Odyssey (for Ocean DYnamicS obSErvation analYsis) is a recently created team involving researchers from Inria (Rennes, France), Ifremer (Brest) and IMT Atlantique (Brest).
Inria is one of the leading research institute in Computer Sciences in France, and Odyssey is also affiliated to the mathematics research institute of the Rennes University (IRMAR).
The team expertise encompasses mathematical (stochastic) and numerical modelling of ocean flows, observational and physical oceanography, data assimilation and machine learning.
Gathering this large panel of skills, the team aims at improving our understanding, reconstruction and forecasting of ocean dynamics, and more specifically to bridge model-driven and observation-driven paradigms to develop and learn novel representations of the coupled ocean-atmosphere dynamics ocean models.
Mission confiée
For accurate climatic predictions, it is essential to have plausible forecasts of the future ocean state. Ideally, high-resolution ocean simulations would be used for this purpose. However, due to their associated computational costs, this approach is currently infeasible, and we must rely only on large-scale ocean representations.
To address this challenge and the urgent need to generate various likely scenarios, there has been a growing interest in geophysical sciences and climate studies in developing flow models that incorporate noise to account for modelling uncertainties or errors.
The introduction of noise into ocean dynamics models must be done on a theoretically rigorous ground. Ad-hoc choices for model noise can fundamentally disrupt the corresponding fluid dynamics models, leading to unrealistic properties. Rigorously justified methodologies for deriving stochastic dynamics models have been recently introduced in the Odyssey team within the ERC STUOD and a longstanding collaboration with Imperial College and Ifremer.
The theoretical framework on which we rely, referred to as "modelling under location uncertainty", decomposes the flow in terms of a resolved smooth component and a rapidly oscillating random component.The stochastic dynamics is then defined from a stochastic representation of the Reynolds transport theorem.From this modelling principle, stochastic equivalents of the classical geophysical flow models can be defined.
A set of models ranging from multi-layers quasi-geostrophic models to primitive equations have been in this way defined and numerically implemented. Ensemble data assimilation are currently under development as well as simplified ocean atmosphere coupled models.
The present post-doc position aims to explore the mathematical analysis of a variational formalism [3], recently proposed by A. Debussche and E. Mémin for the incompressible Euler equation, to infer a dynamics for the noise term, and more specifically, the correlation tensor involved in the definition of the small-scale component. The objective will be to extend this methodology to ocean dynamics and to study theoretically the corresponding system of equations. The second objective will be to explore more specifically such a system for wave-current interaction description. This post-doc will complement the theoretical efforts of the team on oceanic dynamics recently performed in [1,2]
1 A Debussche, É Mémin, A Moneyron, Interpretation of stochastic primitive equations with relaxed hydrostatic assumption as a higher order approximation of 3D stochastic Navier-Stokes arXiv preprint arXiv:2602.04422
2 A Debussche, É Mémin, Antoine Moneyron, Stochastic interpretations of the oceanic primitive equations with relaxed hydrostatic assumptions. Atti Accad. Naz. Lincei Cl. Sci. Fis. Mat. Natur, 2026
3 A Debussche, E Mémin, Variational principles for fully coupled stochastic fluid dynamics across scales
Physica D: Nonlinear Phenomena 481, 134777, 2025
Principales activités
This position will take place in the Odyssey group in Rennes, and will collaborate with Arnaud Debussche and Etienne Mémin.
Compétences
The candidate should have a solid background in applied mathematics and in fluid dynamics dynamics. He/She should have knowledge on stochastic parameterization. She/he must have a good knowledge of Fortran, Python, Pytorch.
Avantages
Rémunération
Starting from €2,695 gross per month, based on your experience
The Inria Centre at Rennes University is one of Inria's nine centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Contexte et atouts du poste
The Odyssey team is offering a 12 month position on ocean modelling within the ERC Stuod (Stochastic transport in ocean dynamics). Odyssey (for Ocean DYnamicS obSErvation analYsis) is a recently created team involving researchers from Inria (Rennes, France), Ifremer (Brest) and IMT Atlantique (Brest).
Inria is one of the leading research institute in Computer Sciences in France, and Odyssey is also affiliated to the mathematics research institute of the Rennes University (IRMAR).
The team expertise encompasses mathematical (stochastic) and numerical modelling of ocean flows, observational and physical oceanography, data assimilation and machine learning.
Gathering this large panel of skills, the team aims at improving our understanding, reconstruction and forecasting of ocean dynamics, and more specifically to bridge model-driven and observation-driven paradigms to develop and learn novel representations of the coupled ocean-atmosphere dynamics ocean models.
Mission confiée
For accurate climatic predictions, it is essential to have plausible forecasts of the future ocean state. Ideally, high-resolution ocean simulations would be used for this purpose. However, due to their associated computational costs, this approach is currently infeasible, and we must rely only on large-scale ocean representations.
To address this challenge and the urgent need to generate various likely scenarios, there has been a growing interest in geophysical sciences and climate studies in developing flow models that incorporate noise to account for modelling uncertainties or errors.
The introduction of noise into ocean dynamics models must be done on a theoretically rigorous ground. Ad-hoc choices for model noise can fundamentally disrupt the corresponding fluid dynamics models, leading to unrealistic properties. Rigorously justified methodologies for deriving stochastic dynamics models have been recently introduced in the Odyssey team within the ERC STUOD and a longstanding collaboration with Imperial College and Ifremer.
The theoretical framework on which we rely, referred to as "modelling under location uncertainty", decomposes the flow in terms of a resolved smooth component and a rapidly oscillating random component.The stochastic dynamics is then defined from a stochastic representation of the Reynolds transport theorem.From this modelling principle, stochastic equivalents of the classical geophysical flow models can be defined.
A set of models ranging from multi-layers quasi-geostrophic models to primitive equations have been in this way defined and numerically implemented. Ensemble data assimilation are currently under development as well as simplified ocean atmosphere coupled models.
The present post-doc position aims to explore the mathematical analysis of a variational formalism [3], recently proposed by A. Debussche and E. Mémin for the incompressible Euler equation, to infer a dynamics for the noise term, and more specifically, the correlation tensor involved in the definition of the small-scale component. The objective will be to extend this methodology to ocean dynamics and to study theoretically the corresponding system of equations. The second objective will be to explore more specifically such a system for wave-current interaction description. This post-doc will complement the theoretical efforts of the team on oceanic dynamics recently performed in [1,2]
1 A Debussche, É Mémin, A Moneyron, Interpretation of stochastic primitive equations with relaxed hydrostatic assumption as a higher order approximation of 3D stochastic Navier-Stokes arXiv preprint arXiv:2602.04422
2 A Debussche, É Mémin, Antoine Moneyron, Stochastic interpretations of the oceanic primitive equations with relaxed hydrostatic assumptions. Atti Accad. Naz. Lincei Cl. Sci. Fis. Mat. Natur, 2026
3 A Debussche, E Mémin, Variational principles for fully coupled stochastic fluid dynamics across scales
Physica D: Nonlinear Phenomena 481, 134777, 2025
Principales activités
This position will take place in the Odyssey group in Rennes, and will collaborate with Arnaud Debussche and Etienne Mémin.
Compétences
The candidate should have a solid background in applied mathematics and in fluid dynamics dynamics. He/She should have knowledge on stochastic parameterization. She/he must have a good knowledge of Fortran, Python, Pytorch.
Avantages
- Subsidized meals
- Partial reimbursement of public transport costs
- Possibility of teleworking (90 days per year) and flexible organization of working hours
- Partial payment of insurance costs
Rémunération
Starting from €2,695 gross per month, based on your experience
