海洋动力学中随机模型的逆问题博士职位

PhD Position F/M Inverse problems for stochastic models under location uncertainty applied to ocean dynamics

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Contract type : Fixed-term contract

Level of qualifications required : Graduate degree or equivalent

Fonction : PhD Position

Level of experience : Recently graduated

Context

The PhD thesis will be supervised by Gilles Tissot (Inria Rennes) and Quentin Jamet (SHOM), and directed by Étienne Mémin (Inria Rennes), within the Inria “Odyssey” team in Rennes. The thesis is part of the ANR “NOUILLES” project, in collaboration with the Inria “AIRSEA” team (Grenoble), the “Odyssey” team (Rennes), IGE (Grenoble), SHOM (Brest), and IRD-LEGOS (Toulouse).

The inter-institutional “Odyssey” team, which brings together researchers from Ifremer, the Laboratory of Physical and Spatial Oceanography (UMR 6523), IMT Atlantique in Brest, and the Inria center at the University of Rennes, will provide the candidate with a particularly rich research environment and numerous opportunities for collaboration. The team aims to develop innovative and cross-disciplinary research directions combining satellite observations, physical modelling, applied mathematics, and numerical methods, with the goal of analysing observational and numerical modelling data and improving our understanding and knowledge of ocean dynamics.

Assignment

Modelling under location uncertainty considers a budget of conserved quantities (mass, momentum, and energy) submitted to a fluid displacement perturbed by Brownian motion. Applying this principle leads to a generalization of the equations governing geophysical fluid dynamics, in which the stochastic terms make it possible to model unresolved subgrid-scale processes that need to be parameterized in ocean models.

The specification of the covariance of the stochastic noise nevertheless remains an open question. The objective of this PhD thesis is to formulate inverse problems aimed at determining these covariances, such that the stochastic model reproduces, according to a criterion to be defined, the behaviour of a reference simulation. The stochastic nature of the models makes the formulation of such inverse problems non-trivial. Furthermore, numerical scalability of the proposed methods to the high dimension of numerical ocean models will be a crucial challenge.

Deep convection will be considered as a case study in this thesis. This phenomenon occurs when water masses cool at the surface, increase in density, and sink in the form of plumes due to gravitational instability. Although these processes occur at scales too small to be resolved by climate models, they nevertheless play a major role in global ocean circulation. The methodologies developed during this thesis could thus provide a basis for parameterizing these processes while combining mathematical rigour with physical consistency.

Main activities

This PhD project will involve theoretical developments, physical modelling, and their numerical implementation.

The candidate will become familiar with stochastic models applied to deep convection events. They will work with reference numerical simulations using a Large-Eddy Simulation (LES) approach, performed with the operational CROCO model. They will theoretically formulate inverse problems tailored to this class of models. Adjoint and ensemble methods will be considered, and different choices of control parameters and cost functions will be compared.

Determining the covariance of the stochastic noise conditioned on environmental parameters, such as surface heat flux or stratification, will be a major focus, both from a theoretical perspective and in terms of physical applications. To assess the scalability of the developed methods, a canonical three-dimensional turbulent channel flow configuration, without rotation or stratification, may also be considered.

Skills

A background in applied mathematics, fluid mechanics, or oceanography, together with a strong interest in numerical simulation, is required.

Basic programming skills in Python or Julia and Fortran or C++ would be an advantage.

Benefits package

• Restauration subventionnée • Transports publics remboursés partiellement • Congés: 7 semaines de congés annuels + 10 jours de RTT (base temps plein) + possibilité d'autorisations d'absence exceptionnelle (ex : enfants malades, déménagement) • Possibilité de télétravail (après 6 mois d'ancienneté) et aménagement du temps de travail • Équipements professionnels à disposition (visioconférence, prêts de matériels informatiques, etc.) • Prestations sociales, culturelles et sportives (Association de gestion des œuvres sociales d'Inria) • Accès à la formation professionnelle • Sécurité sociale

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General Information

• Theme/Domain : Earth, Environmental and Energy Sciences

Scientific computing (BAP E)

• Town/city : Rennes

• Inria Center :

Centre Inria de l'Université de Rennes

• Starting date : 2026-11-01

• Duration of contract : 3 years

• Deadline to apply : 2026-11-07

Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.

Instruction to apply

Defence Security :

This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.

Recruitment Policy :

As part of its diversity policy, all Inria positions are accessible to people with disabilities.

Contacts

• Inria Team :

ODYSSEY

• PhD Supervisor :

Tissot Gilles / gilles.tissot@inria.fr

About Inria

Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.