PhD Position F/M Data assimilation of satellite data in oceanic models, learning of oceanic dynamics

The Inria Rennes - Bretagne Atlantique Centre is one of Inria's eight centres and has more than thirty research teams. The Inria Center 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.

The Odyssey team is offering  a PhD position on numerical ocean dynamics simulation, machine learning and data assimilation.

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.

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 PhD position aim to explore: data driven dynamics specification and learning from high-resolution data as well as the devising of hierarchical data assimilation ensemble strategies to couple stochastic ocean model and high resolution satellite data such as the SWOT data

The PhD student will collaborate directly with the Odyssey group in Rennes (Noé Lahaye, E. Mémin, Gilles Tissot) and in Brest (B. Chapron and R. Fablet). He will be supervised by Etienne Mémin and co-supervised by Bertrand Chapron and Ronan Fablet to cover different aspects including stochastic modelling of ocean dynamics, satellite observation and machine learning for ocean dynamics and data assimilation. 

He/She will be part of a small group devoted to ensemble method for forecast, learning and data assimilation of ocean dynamics.  Her/His work will undergo also strong collaborations with the Odyssey group at IMT Atlantique (R. Fablet) as well as with the other PI of the ERC Stuod group (Bertrand Chapron, Dan Crisan, Darryl Holm). During this PhD position we will explore in particular ensemble methods and kernel representation and data assimilation. 

The candidate should have a solid background in applied mathematics and/or in fluid mechanics and/or in geophysical dynamics.

She/he must have a good knowledge of Fortran, C/C+/ Python, Pytorch.

• 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

Monthly gross salary amounting to 2100 euros for the first and second years and 2200 euros for the third year