Organisation/Company: CNRS
Department: Institut Terre Environnement Strasbourg
Research Field: Physics
Researcher Profile: First Stage Researcher (R1)
Country: France
Application Deadline: 27 Nov 2024 - 23:59 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 2 Dec 2024
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure? No
Offer Description
The thesis is funded by the CNRS Prime 80 project through the CNRS Mission for Cross-disciplinary and Interdisciplinary Initiatives. The thesis will be jointly supervised by the Institute of Earth and Environment in Strasbourg (ITES) and the Jean Kuntzmann Laboratory (LJK) in Grenoble. It will involve adapting mathematical tools developed at the LJK to calibrate a hydrological model developed at the ITES. The student will be based in Strasbourg and will undertake stays in Grenoble to familiarize themselves with the mathematical tools. The student will have the opportunity to participate in scientific events organized by research infrastructures such as OZCAR and the Uncertainty Quantification Research Network.
Enrollment in PhD program: University of Strasbourg
Academic and industrial secondments: University of Grenoble Alpes
Responsibilities
Calibration of a hydrogeological model through assimilation of geophysical data.
Objectives
Distributed hydrological models (DHM) are particularly suitable for providing reliable estimates of future changes in groundwater storage at the catchment scale. However, accurately modelling processes is particularly challenging due to limited observations of processes occurring in the subsurface environment. An important challenge is to define the spatial variability of subsurface parameters that contribute to the DHM parameters. The thesis aims to develop a robust numerical solver for the hydrogeological inverse problem, allowing integration of geophysical data suitable for catchment-scale applications. These data consist of magnetic resonance sounding and gravimetric measurements, respectively detecting spatially varying water storage and temporal dynamics of groundwater masses. Ensemble Kalman filter-based assimilation tools will be adapted to the hydrological model and the geophysical dataset to be adjusted.
Expected Results
The thesis will involve developing and validating the reliability of a data assimilation tool coupling a DHM applied at the catchment scale with geophysical data. The tool will be tested on digital catchments before being applied to a real catchment, the Strengbach. Applying the developed tool will enable estimation of DHM parameters for this catchment and provide an estimation of spatial and temporal variations in groundwater storage within this catchment.
Skills
Enthusiasm and motivation to work on numerical codes.
Interest in collaborative research, open science, and implementation of reusable codes for use by an extended scientific community.
Master's degree or equivalent in Hydrology, Geophysics, Data Science, Applied Mathematics, Computer Science, or related disciplines. Understanding of catchment hydrology appreciated.
English level B2, communication skills; proficiency in French is an asset but not essential.
Experience in model development, sensitivity analysis, or model calibration via inverse problem resolution methods will be highly appreciated.
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