The Max Planck Institute for Biogeochemistry (MPI-BGC) is inviting applications for a PhD position on combining physically-based modeling and deep learning for coupled water-carbon cycle modeling (m/f/d) in full-time, 3 years for the Global Diagnostic Modeling group within the Department of Biogeochemical Integration. This project intends to develop a hybrid model of the coupled global water and carbon cycles. The candidate (m/f/d) will explore pathways to constrain the model with various Earth observation datasets and prior knowledge. The project is conducted in close collaboration with a companion PhD project at the Technical University of Munich (TUM) Computer Vision Research Group at the Chair of Remote Sensing Technology, which focusses on formal uncertainty quantification of deep neural networks.
- Applicant must be motivated (m/f/d) with a strong interest in Earth sciences, data analysis, and machine learning. The successful candidate (m/f/d) will work in close collaboration with an international and diverse research team.
- Applicant must hold Master’s degree (or equivalent) in Computer Science, Geoinformatics, Geosciences, Remote Sensing, or similar.
- Strong programming skills, preferably in Python
- Knowledge in processing and analyzing large data sets, machine-learning, statistical analysis
- Ideally experience with deep learning frameworks (e. g., PyTorch)
- Self-driven personality able to work both independently and in a team
- Excellent oral and written communication skills in English
Application to be sent by e-mail to firstname.lastname@example.org with cover letter, curriculum vitae as well as names and contact information of two references summarised in a PDF file (max. 10 MB).
|Organization||Max Planck Institute for Biogeochemistry (MPI-BGC)|
|Subject areas||Computer Science, Geoinformatics, Geosciences, Remote Sensing|
|Eligibility||Open to all nationalities|
|Deadline||September 30, 2021|