M/F Coupling machine learning and advanced numerical techniques for the simulation of Hydrogen [...]

European Commission; France; Sur place; EUR 25 000 - 35 000; Plein temps

Résumé du poste

A leading research institute in France is offering a PhD position focused on developing numerical models for hydrogen migration in porous media. Candidates should have strong skills in numerical analysis and scientific computing, with an interest in machine learning. The project addresses the challenges of hydrogen storage, utilizing advanced simulations to explore complex processes.

Qualifications

• Experience with numerical analysis and discretization methods required.
• Knowledge of flow and transport in porous media is an asset.

Responsabilités

• Develop advanced numerical models for hydrogen migration in porous domains.
• Couple numerical schemes with machine learning to improve traditional methods.

Connaissances

Numerical Analysis

Scientific Computing

Machine Learning

Formation

PhD

Description du poste

Organisation/Company: CNRS

Department: Institut Terre Environnement Strasbourg

Research Field: Environmental science, Biological sciences, Geosciences

Researcher Profile: First Stage Researcher (R1)

Country: France

Application Deadline: 4 Jun 2025 - 23:59 (UTC)

Type of Contract: Temporary

Job Status: Full-time

Hours Per Week: 35

Offer Starting Date: 1 Oct 2025

Funding: Not funded by a EU programme

Research Infrastructure Staff Position: No

Offer Description

The PhD will be carried out at the Earth and Environmental Sciences Institute in Strasbourg. The laboratory is located on the main campus, which has an on-site university dining facility. The site is easily accessible by public transportation.

The candidate will be officially enrolled at the University of Strasbourg, under the Doctoral School of Earth and Environmental Sciences (ED413). The thesis is co-supervised by Brahim Amaziane from the University of Pau.

Required skills include numerical analysis, discretization methods, scientific computing, with additional experience in machine learning. Knowledge of flow and transport in porous media is considered an asset. Please provide a CV, cover letter, and reference letters.

Research Context

Storage of hydrogen in aquifers or geological reservoirs is gaining attention. Despite experience with storing compressed air, natural gas, and town-gas, hydrogen storage presents unique challenges due to its physical and biochemical properties. Numerical simulations using physics-based models are essential to address these challenges, involving complex multi-physical processes like two-phase flow, precipitation, dissolution, and thermodynamics.

The project aims to develop advanced numerical models for hydrogen migration in porous domains, coupling sophisticated numerical schemes with machine learning to overcome the limitations of traditional methods. This research addresses the nonlinear and coupled nature of the processes involved, including the effects of low density, viscosity, reactivity, and thermal processes of hydrogen.