A fully funded PhD position is available at Bergische Universität Wuppertal, Germany, in the field of Bayesian hierarchical modeling for climate reconstruction. The research is part of the DFG-funded ICEBAY project, embedded in the DFG Priority Programme SPP 1158 (Antarctic Research), and focuses on using Bayesian modeling and probabilistic inference to address inverse problems in climate science. The project utilizes ice-core data and borehole thermometry to reconstruct past climate conditions, emphasizing uncertainty-aware approaches and hierarchical modeling techniques.

Professor Peter Zaspel, Professor for Software in Data-Intensive Applications, is the academic supervisor for this position. Candidates interested in Bayesian modeling, probabilistic inference, and climate reconstruction are encouraged to apply. Before submitting a formal application, candidates are invited to participate in a small research-style programming challenge designed to introduce the core ideas of the project and provide hands-on experience with Bayesian inverse problems. Starter code is provided, and applicants are welcome to experiment with different modeling approaches.

The position is open to international applicants, with English as the working language. The successful candidate will receive full funding for up to three years under the TV-L E13 pay scale. The application deadline is January 19, 2026, and applications should be submitted via the University of Wuppertal job portal, referencing number 25354. This opportunity is ideal for students with a strong background in Bayesian modeling, statistics, environmental science, or related fields, and who are eager to contribute to cutting-edge research in climate reconstruction and Antarctic science.