Claudia Stephan
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PhD Student: Generative Diffusion Models for Atmospheric Data Assimilation Leibniz Institute of Atmospheric Physics in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Apr 14, 2026
Country
Germany
University
Leibniz Institute of Atmospheric Physics

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Where to contact
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About this position
The Leibniz Institute of Atmospheric Physics (IAP) is offering a part-time (75%) PhD student position in the Department of Modelling of Atmospheric Processes, focusing on generative diffusion models for atmospheric data assimilation. This three-year position, starting as soon as possible, is designed for candidates interested in advancing scientific knowledge of the mesosphere and lower thermosphere through innovative machine learning and modelling approaches. The successful candidate will develop and train generative diffusion models to reverse diffusion processes, enabling efficient data assimilation for atmospheric fields. The research involves generating atmospheric fields for specific time instances that closely match pointwise observations, training models on circulation fields from numerical simulations, and exploring strategies for generating full 3D fields or decomposing tasks into 2D layers to optimize GPU memory usage.
Applicants should hold a completed MSc degree in mathematics, informatics, physics, or a related field, with strong expertise in machine learning and high performance computing on CPUs and GPUs. Proficiency in Fortran, Python, shell scripting, and Linux command-line environments (including remote systems via SSH) as well as Windows is required. Interest in modern software engineering practices and fluency in English are essential. The institute offers an attractive working environment near the Baltic Sea, modern equipment, engagement in an international research community, participation in the company pension scheme (VBL), flexible working hours, and mobile working options. The employment relationship follows the Collective Agreement for the Public Service of the Federal States (TV-L).
IAP is committed to advancing atmospheric physics, instrumentation, analysis, and modelling to address societal needs such as climate change. The institute collaborates closely with the University of Rostock and is part of the teaching program of the Institute of Physics. As a member of the Leibniz Association, IAP values family friendliness, equality of opportunity, and flexibility. Qualified women and people with disabilities are encouraged to apply.
To apply, submit a motivational letter, CV, diploma with final grade, certificates, testimonies, and references, indicating the keyword 2026-03, to [email protected]. Applications received before April 14, 2026, will be fully considered. For further information, contact Prof. Dr. Claudia Stephan ([email protected]) or visit the institute website. Please note that application and travel costs cannot be covered by the state of Mecklenburg-Vorpommern.
Funding details
Available
What's required
Applicants must have a completed MSc university degree in mathematics, informatics, physics, or a related field. Strong expertise in machine learning and high performance computing on CPUs and GPUs is required. Proficiency in Fortran, Python, shell scripting, and Linux command-line environments (including remote systems via SSH) as well as Windows is expected. Interest in modern software engineering practices (testing, code review, modular design) and fluency in English as the working language are necessary. Applicants should submit a motivational letter, curriculum vitae, diploma with indication of final grade, and copies of certificates, testimonies, and references.
How to apply
Send your application documents (motivational letter, CV, diploma with final grade, certificates, testimonies, references) with the keyword 2026-03 to [email protected]. Applications received before April 14, 2026, will receive full consideration. For further information, contact Prof. Dr. Claudia Stephan or visit the institute website. Use the provided application link for more details.
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