Publiée 19 juin 2026
Post-Doctorant F/H Tidal resource assessmen, uncertainty quantification and optimization in the Alderney Race
Inria
Paris, Île-de-France 75000, France
CDI
Contexte et atouts du poste
The ERRABL project, funded in the context of the Maths-Vives PEPR program ( https://www.maths-vives.fr/ ) and in collaboration with INRIA Paris and University of Caen, aims to improve the reliability and computational efficiency of hydrodynamic models used to assess tidal-stream energy resource, focusing on the Alderney Race ( Raz Blanchard ), in Normandy, one of Europe's most energetic tidal sites. High-fidelity computational models such as Telemac3D (used and developed at University of Caen, https://www.opentelemac.org/index.php/presentation?id=18 ) provide accurate simulations of tidal flows but remain computationally expensive and uncertain due to complex physics (e.g. turbulence, bottom friction) and uncertain inputs (bathymetry, tidal forcing). The computational cost becomes prohibitive when attempting to model the flow over several tidal cycles within a tidal turbine farm, where the turbines are represented using actuator disk models.
To overcome these limitations, ERRABL develops hybrid model-data approaches and metamodels (surrogate models) that (i) correct physical models using data assimilation and machine learning, (ii) quantify uncertainties on key physical quantities such as the Annual Energy Production (AEP), and (iii) enable optimization of tidal array layout design under uncertainty (i.e., determining the optimal number and placement of turbines within a given area while accounting for hydrodynamic interactions between the turbines).
In this framework, a postdoc hired by INRIA and detached at Institut Jean Le Rond D'Alembert, the Mechanics Institute of Sorbonne University, will be in charge of the development of data-driven improvements to hydrodynamic models, the development of surrogate models for fast wake field modeling, and his use for the optimization of tidal array layout design under highly uncertain conditions.
Mission confiée
The postdoctoral fellow will play a central role in Objective 2 ("Metamodel, uncertainty propagation and optimization") of ERRABL. The main goals are:
1. Data-Driven Improvement of physical submodels in Telemac
2. Multi-fidelity surrogate modeling for fast wake field modeling
3. Optimization of tidal array layout design.
Principales activités
The postdoctoral project aims to develop data-driven homogenized models that represent the cumulative impact of tidal turbine wakes at the farm scale, with the explicit goal of predicting how turbine placement density and spatial distribution influence power extraction and flow modification. Instead of resolving individual turbine wakes or their detailed dynamics, the project targets aggregate wake-induced effects, enabling fast and reliable evaluation of tidal array configurations.
The research will leverage data from high-fidelity numerical simulations and/or experimental measurements, including reference simulations based on actuator disk representations, to learn effective relationships between turbine density, environmental conditions, and farm-level performance indicators. These models will encapsulate wake-induced losses and interactions in a reduced, aggregated form.
A key emphasis will be placed on robustness, generalization across operating conditions, and physical consistency, ensuring that the learned models remain predictive outside the training configurations. The resulting framework will enable rapid evaluation of turbine array layouts, providing a scalable alternative to more costly discrete-turbine simulations for design, planning, and optimization of tidal energy farms.
Compétences
Langues : anglais
Avantages
The ERRABL project, funded in the context of the Maths-Vives PEPR program ( https://www.maths-vives.fr/ ) and in collaboration with INRIA Paris and University of Caen, aims to improve the reliability and computational efficiency of hydrodynamic models used to assess tidal-stream energy resource, focusing on the Alderney Race ( Raz Blanchard ), in Normandy, one of Europe's most energetic tidal sites. High-fidelity computational models such as Telemac3D (used and developed at University of Caen, https://www.opentelemac.org/index.php/presentation?id=18 ) provide accurate simulations of tidal flows but remain computationally expensive and uncertain due to complex physics (e.g. turbulence, bottom friction) and uncertain inputs (bathymetry, tidal forcing). The computational cost becomes prohibitive when attempting to model the flow over several tidal cycles within a tidal turbine farm, where the turbines are represented using actuator disk models.
To overcome these limitations, ERRABL develops hybrid model-data approaches and metamodels (surrogate models) that (i) correct physical models using data assimilation and machine learning, (ii) quantify uncertainties on key physical quantities such as the Annual Energy Production (AEP), and (iii) enable optimization of tidal array layout design under uncertainty (i.e., determining the optimal number and placement of turbines within a given area while accounting for hydrodynamic interactions between the turbines).
In this framework, a postdoc hired by INRIA and detached at Institut Jean Le Rond D'Alembert, the Mechanics Institute of Sorbonne University, will be in charge of the development of data-driven improvements to hydrodynamic models, the development of surrogate models for fast wake field modeling, and his use for the optimization of tidal array layout design under highly uncertain conditions.
Mission confiée
The postdoctoral fellow will play a central role in Objective 2 ("Metamodel, uncertainty propagation and optimization") of ERRABL. The main goals are:
1. Data-Driven Improvement of physical submodels in Telemac
2. Multi-fidelity surrogate modeling for fast wake field modeling
3. Optimization of tidal array layout design.
Principales activités
The postdoctoral project aims to develop data-driven homogenized models that represent the cumulative impact of tidal turbine wakes at the farm scale, with the explicit goal of predicting how turbine placement density and spatial distribution influence power extraction and flow modification. Instead of resolving individual turbine wakes or their detailed dynamics, the project targets aggregate wake-induced effects, enabling fast and reliable evaluation of tidal array configurations.
The research will leverage data from high-fidelity numerical simulations and/or experimental measurements, including reference simulations based on actuator disk representations, to learn effective relationships between turbine density, environmental conditions, and farm-level performance indicators. These models will encapsulate wake-induced losses and interactions in a reduced, aggregated form.
A key emphasis will be placed on robustness, generalization across operating conditions, and physical consistency, ensuring that the learned models remain predictive outside the training configurations. The resulting framework will enable rapid evaluation of turbine array layouts, providing a scalable alternative to more costly discrete-turbine simulations for design, planning, and optimization of tidal energy farms.
Compétences
Langues : anglais
Avantages
- 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 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
