For PhD students:
1. MSc degree in Bioscience Engineering, Civil or Environmental Engineering, Hydrology, Meteorology, Earth Observation, Physics, Mathematics, Computer Sciences, or equivalent
2. Excellent motivation to work on soil-plant-snow processes, remote sensing and modeling
3. Experience with data-processing or programming in Matlab, Python, IDL, GrADS, R, or other
4. Interest in open source code distribution (e.g. GitHub)
5. Excellent motivation and grades
6. Creative, critical, analytical and innovative mindset
7. Ability to work independently
8. Excellent written and oral communication skills in English
For Postdoctoral candidates:
9. PhD degree in Civil or Environmental Engineering, Bioscience Engineering, Hydrology, Meteorology, Earth Observation, Physics, Mathematics, Computer Sciences, or equivalent
10. Experience with land surface and/or atmosphere and/or snow processes and modeling
11. Experience with remote sensing, Earth observation, large datasets
12. Experience in statistics, including some notions on data assimilation
13. Experience with data-processing software such as Python, Matlab, IDL, GrADS, R, or other
14. Experience with programming and scientific computing in a language such as Fortran or C
15. Experience with high-performance computing in a Linux environment
16. Excellent motivation and grades
17. Creative, critical, analytical and innovative mindset
18. Ability to work independently and lead a small research group
19. Excellent written and oral communication skills in English, proven in publications
20. Experience with working with Git/Github is an advantage
Remote sensing of snow water equivalent (SWE) has been notoriously difficult. Even though some estimates can be obtained from passive and active microwave sensors, the snow-radiation interactions are not yet fully understood and therefore limit the snow retrieval quality. In the proposed research, we will learn the microwave interactions with snow via machine learning and perform a direct assimilation of active and passive microwave signals into a land surface model. This is expected to improve both the snow depth and SWE estimates over mountain regions. Next, these improved snow estimates will be used to initialize weather forecasts with a coupled land-atmosphere model. More specifically, the land surface data assimilation will be performed with Noah-MP within NASA’s LIS and the coupled land-atmosphere simulations will be done with NU-WRF. Next to the focus on research, the successful candidate will
21. perform and disseminate high quality research related to snow remote sensing, land surface modeling, data assimilation, machine learning, land-atmosphere coupling
22. supervise PhD and/or master thesis students
23. for PhD students: follow training in line with the doctoral school requirements
KU Leuven is looking for an enthusiastic PhD student or postdoctoral researcher with interest or experience (respectively) in (i) snow modeling or snow remote sensing, (ii) data assimilation or machine learning, and (iii) land-atmosphere interactions. You will be part of the Department of Earth and Environmental Sciences, Division Soil and Water Management, Research Group “Land Surface Remote Sensing, Modeling and Data Assimilation” (RSDA) at the Katholieke Universiteit Leuven (KU Leuven), and collaborate with the Hydro-Climate Extremes Lab (H-CEL) Research Group within the Department of Environment at Ghent University (UGent). The candidate will be jointly supervised by prof. dr. ir. Gabriëlle De Lannoy and dr. ir. Hans Lievens. Therefore, the PhD student will obtain a joint degree at KU Leuven – UGent. The postdoc is expected to work in a broad international context, and collaborate with PhD and MSc students.
24. EITHER: fully funded PhD scholarship for 4 years; support and training through the Arenberg Doctoral School (https://set.kuleuven.be/phd); students graduated with an MSc degree in the summer 2025 are encouraged to apply
25. OR: postdoc position for 2 years, with a possibility for 1 year extension
26. The start date can be negotiated, but is ideally in the fall of 2025.
27. Competitive salary, support in career development
28. Multi-disciplinary and international professional environment
29. Leuven is a charming historical university town, located in the heart of Western Europe