Max Planck Institute for Biogeochemistry
3 months ago
PhD Position in AI for Hydro-Climatic Extremes at Max Planck Institute for Biogeochemistry Max Planck Institute for Biogeochemistry in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
The PhD position is full-time, fixed-term for 3 years, and will be evaluated and graded according to the TVöD Bund collective agreement. The position is funded by the Carl-Zeiss-Stiftung. The offer includes collaboration with domain and machine learning experts, and part-time work is generally possible. Specific stipend or salary amounts are not mentioned.
Deadline
Expired
Country
Germany
University
Max Planck Institute for Biogeochemistry

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About this position
The Max Planck Institute for Biogeochemistry (MPI-BGC) in Jena, Germany, is offering a fully funded PhD position in the field of AI for Hydro-Climatic Extremes as part of the GENAI-X project. This interdisciplinary research opportunity focuses on developing hybrid machine learning approaches to improve the generalizability of flood and hydro-climatic models under non-stationary environmental conditions. The GENAI-X project, funded by the Carl-Zeiss-Stiftung, brings together experts from the Max Planck Institute for Biogeochemistry, Friedrich Schiller University Jena, Jena University Hospital, and the Senckenberg Institute for Plant Form and Function. The project aims to address the challenge of robust model generalizability in dynamic Earth system sciences, with a particular emphasis on hydro-climatic extremes such as floods, droughts, and landslides.
The successful candidate will contribute to the flood-related subproject, developing machine learning models for dynamic, non-stationary processes relevant to flood occurrence and response. The work will involve combining data-driven methods with hydrologic process representations, improving model generalizability across climatic regimes, and quantifying flood risk under changing climate and land-surface conditions. The position offers the chance to work with a multidisciplinary team and to present results in publications and project communications.
Applicants should have a Master’s degree in Computer Science, Data Science, AI, Mathematics, Physics, Geoinformatics, or a related quantitative field. Candidates from environmental sciences are also welcome if they possess strong programming skills, preferably in Python. Experience with machine learning for time series, geospatial data, or dynamic models is required, and familiarity with deep learning frameworks such as PyTorch is ideal. Strong analytical and conceptual skills, the ability to work independently and in a team, and excellent English communication skills are essential.
The PhD position is full-time, fixed-term for three years, and is graded according to the TVöD Bund collective agreement. The start date is expected to be April 2026, and part-time work is possible. The Max Planck Society is committed to gender equality and diversity, and applications from women and severely disabled persons are encouraged. The application deadline is January 3, 2026.
To apply, candidates should submit a cover letter, CV, and contact information for two references in a single PDF (max 10 MB) by email to [email protected], quoting reference number 20/2025. For further inquiries, contact Shijie Jiang at [email protected]. For more information about the project and the institute, visit the official job description and project pages linked above.
Funding details
The PhD position is full-time, fixed-term for 3 years, and will be evaluated and graded according to the TVöD Bund collective agreement. The position is funded by the Carl-Zeiss-Stiftung. The offer includes collaboration with domain and machine learning experts, and part-time work is generally possible. Specific stipend or salary amounts are not mentioned.
What's required
Applicants must hold a Master's degree in Computer Science, Data Science, AI, Mathematics, Physics, Geoinformatics, or a related quantitative field. Candidates from environmental sciences are welcome if they have strong programming skills, preferably in Python. Experience with machine learning for time series, geospatial data, or dynamic models is required; ideally, candidates have experience with deep learning frameworks such as PyTorch. Strong analytical and conceptual skills for designing and interpreting ML experiments in complex environmental settings are expected. Applicants should be self-driven, able to work independently and in a team, and possess excellent oral and written communication skills in English.
How to apply
Submit your application with a cover letter, CV, and contact information for two references in a single PDF (max 10 MB) by email to [email protected], quoting reference number 20/2025. Applications must be received by January 3, 2026. For inquiries, contact Shijie Jiang at [email protected].
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