6 month Engineer position Stochastic modelling and numerical simulation of coupled oceanic flow, small-scale dynamics, wave-current interaction

1000scholars; Rennes; On-site; EUR 26,000 - 38,000; Full time

Job summary

The Odyssey team in Rennes invites applications for a 6-month engineer position on ocean modelling within the ERC Staod (Stochastic transport in ocean dynamics). The role focuses on implementing stochastic dynamics models in ocean flows and conducting numerical experiments in collaboration with Odysseys researchers.

Ideal candidates have a strong background in applied mathematics and fluid dynamics, with proficiency in Fortran, Python, and PyTorch, and will work within the Odyssey group in

Qualifications

• Solid background in applied mathematics and fluid dynamics.
• Knowledge of stochastic parameterization.
• Proficiency in Fortran, Python, and PyTorch.

Responsibilities

• Implement stochastic variational models in ocean dynamics.
• Conduct numerical experiments in idealized scenarios.
• Collaborate with Odyssey group in Rennes and with collaborators.

Skills

Fortran

Python

Pytorch

Stochastic parameterization

Fluid dynamics

Applied mathematics

Job description

Context

The Odyssey team is offering a 6 month engineer 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.

Assignment

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. The present engineer position aims to assess numerically coupled stochastic variational models proposed recently. Originally proposed for the Euler equations by A. Debussche and E. Mémin for the incompressible Euler equation, the formalism has been very recently extended to oceanic flow models. The objective will consist in implementing such systems in idealized situations.

Main activities

The engineer position will take place in the Odyssey group in Rennes, and will collaborate with Arnaud Debussche and Etienne Mémin.

Skills

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.