Postdoctoral Researcher (m/f/d; E13 TV-L) - Machine Learning and Biogeosciences

The Chair for Machine Learning in Science at the University of Tübingen has an opening for a

Postdoctoral Researcher (m/f/d; E13 TV-L)
to work at the intersection of
Machine Learning and Biogeosciences

Come work with us and use deep learning tools and LLMs to build powerful mechanistic models for fire in regional to global vegetation models. A special focus lies on the impacts of fire-mediated impacts of climate change on vegetation and ecosystem processes as well as potential societal adaptation measures. The project is within the TERRA excellence cluster () and a collaboration of the Machine Learning in Science lab (Dr. Cornelius Schröder, Prof. Dr. Jakob Macke) and the Biogeography and Ecosystem Ecology lab of (Dr. Matthew Forrest, Prof. Dr. Thomas Hickler) at the Senckenberg Biodiversity and Climate Research (SBiK-F) Institute in Frankfurt (Main). You will be based in Tübingen, surrounded by a strong ML community, or Frankfurt, with a strong research profile in vegetation, terrestrial biosphere and climate impact modelling, depending on your background and interests.

Candidate qualifications: We are looking for candidates with a strong quantitative background and a PhD degree in a relevant discipline, ideally in machine learning, numerical simulation, or environmental modelling, and a genuine interest in collaborative and interdisciplinary work at the interface of machine learning and biogeosciences, and strong programming skills (ideally Python and relevant deep learning tools; C++ is also a benefit because the vegetation-fire models we work with at SBiK-F are written in C++).

While this project will be an interdisciplinary endeavor, no prior knowledge in biogeosciences is mandatory. However candidates should have a strong interest in questions related to biogeoscientific models, such as fire, vegetation or climate models and are willing to pick up on relevant topics. At least in the longer term, the goal is that the new developments can be integrated within vegetation and terrestrial biosphere models.

Application: Fixed-term contracts will be for one year / until 31 st May 2027 with possible extension; starting date is as soon as possible. Employment will be carried out by the central administration of the University of Tübingen or the Senckenberg Institute.

Please submit your application materials to [email protected] as a single PDF, including a CV with publication list, relevant transcripts, a statement of research interests (max. two pages), contact details of two referees, and a link to a code repository (or work samples). Please apply before April 20, 2026 . Only complete applications will be considered.

Our groups: We () develop machine learning and AI methods to accelerate scientific discovery. We aim to provide an interdisciplinary, collaborative and supportive work environment which emphasizes diversity and inclusion. In addition to making central contributions to these interdisciplinary projects, positions will offer opportunities for developing your own research program. Working language in the group and institute is English.

In the Biogeography and Ecosystem Ecology lab at SBiK-F () we develop mechanistic vegetation and terrestrial biosphere (global scale) models that represent processes, such as the global carbon cycle, the growth and dynamics of forests and their ecosystem services, biodiversity and wildfires. We focus on the interactions between biodiversity, ecosystem functioning and climate,, particularly in the context of man-made climate change.

Scientific environment: We are embedded in Tübingen’s renowned research community in AI, including the Cyber Valley, the Tübingen AI Center, ELLIS, the Excellence Cluster Machine Learning and have strong connections to the new TERRA excellence cluster in geoscience (Terrestrial Geo-Biosphere Interactions in a Changing World). We are situated in the AI Research Building, in close proximity to the Max Planck Institutes, and participate in the International Max Planck Research Schools (IMPRS) ‘Intelligent Systems’.

Institutional commitment to diversity, equity, and inclusion: The university is committed to equal opportunities and diversity and seeks to raise the number of women in research and teaching. We urge qualified women academics to apply for these positions. Equally qualified applicants with disabilities will be given preference in the hiring process.