Doctoral Researcher (f/m/d) part time 75%
InfraStructure for dAta-BasEd Learning in environmental sciences (ISABEL)
During your PhD you will explore new avenues in hydrological modelling, particularly the opportunity of using physics-informed data-driven models for improved predictions of hydrological extremes and their recurrence in time. Specifically, you will explore synergies between data-based and process-based hydrological modelling, key emphasis is on how to use today's manifold datasets such as operational rain gauges, rainfall radar, private weather stations and commercial microwave links for improved flood predictions, or on using Regional Climate Model outputs for an improved assessment of drought risks.
Your research will be embedded in the Project ISABEL, which aims at the expansion and optimization of the virtual research environment V-FOR-WaTer. V-FOR-WaTer already contains generic tools for pre-processing and statistics of hydro-meteorological data and an advanced geostatistics toolbox. One aim of ISABEL is to connect the worlds of hydrological models and machine learning models, and to facilitate their joint use as much as possible. ISABEL rests on a close collaboration between the Institute of Water and River Basin Management (IWG) and the Steinbuch Computing Centre (SCC) at KIT. You will complement the existing team of computer scientists and hydrologists and work together closely with another PhD student at IWG. The technical implementation of models and tools into the V-FOR-WaTer infrastructure will be supported by a software developer.
Salary category EG 13, depending on the fulfillment of professional and personal requirements.
Institute for Water and River Basin Management (IWG)
1. January 2023
Contact person in line-management
For further information, please contact Dr. Sibylle Haßler, email: email@example.com .
Please apply via email to: firstname.lastname@example.org including a detailed CV, scans of degree certificates, a letter of motivation, and contact information for two referees.
Vacancy number: 2280/2022
We prefer to balance the number of employees (f/m/d). Therefore, we kindly ask female applicants to apply for this job. Recognized severely disabled persons will be preferred if they are equally qualified.