E-2426 PHD IN USING EXPERIMENTAL DATA AND MACHINE LEARNING TO BENCHMARK HYD

at  Luxembourg Institute of Science and Technology LIST

HOW WILL YOU CONTRIBUTE?

During extreme hydrological conditions (floods and droughts), water fluxes can be regulated by processes that are difficult to observe during regular flow conditions. You will use over 20 years of data collected by a network of nested experimental catchments and existing process knowledge to test hydrological models and analyze water fluxes during extreme events across different geologies. By leveraging machine learning techniques, you will develop methods to highlight the limitations of conventional models during such extreme events. The objective is to evaluate and improve hydrological models, creating tools to reliably describe extreme events that shall ultimately contribute to improved risk management strategies.
You will carry out your doctoral research project in the framework of the JCAR ATRACE project (https://jcar-atrace.eu). You will be part of a community of PhD students from multiple affiliate institutions, all working to reduce risk from extreme hydrological events in central Europe. In this context, you are expected to interact and collaborate with candidates from other partner institutions. Your work will be carried out under the co-supervision of LIST researchers and the University of Luxembourg. Throughout your PhD research project, you will be part of the Catchment and Eco-Hydrology research group at LIST and benefit from the multi-disciplinary expertise in hydrological sciences.
The ‘Environmental Sensing and Modelling’ (ENVISION) unit is an interdisciplinary team of 45+ scientists, engineers, post-docs, and PhD candidates – structured in three complementary groups focusing on agro-environmental systems research, remote sensing, natural resources modeling, and critical zone research.
Embedded into the ENVISION unit, the ‘Catchment and eco-hydrology’ (CAT) research group has its efforts geared towards a holistic understanding of intrinsically coupled hydrological and human systems. At the CAT group, we rely on our competencies in hydrology, geochemistry, sedimentology, and environmental systems engineering to gain a better understanding of eco-hydrological processes controlling hydrological and biogeochemical cycles, vegetation and sediment dynamics, pollutant removal, and ecosystem resilience.
You will hold a secondary affiliation with the Complex Systems Research Group (Team Nexus) at the University of Luxembourg, which will be responsible for awarding the PhD degree. Co-supervision from Team Nexus will complement the environmental expertise of LIST with advanced numerical skills.
The CAT group is seeking a highly motivated PhD candidate to work on the hydrological modeling of extreme events. This position is part of the JCAR ATRACE project, geared towards reducing flood and drought risk in Central Europe. Your research will leverage long experimental datasets for machine learning applications. A major goal consists in the identification of deficiencies proper to conventional hydrological models.

Please refer the Job description for details