Peter Zaspel

Bergische Universität Wuppertal has announced upcoming recruitment for a funded PhD position in Antarctic climate research, supported by the DFG Priority Programme SPP 1158. The project focuses on reconstructing past temperatures in Antarctica by integrating ice-core data, borehole measurements, and advanced statistical modeling. The research is highly interdisciplinary, sitting at the intersection of mathematics, computer science, data science, and climate science. The group collaborates with Prof. Dr. Thomas Laepple from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, bringing together expertise from leading German institutions in polar research. The successful candidate will join a vibrant research community and contribute to cutting-edge environmental science. Applicants should have a strong background in mathematics, computer science, data science, or climate science, with experience in statistical modeling and environmental data analysis considered a plus. Funding for the position is secured, but specific details on stipend and tuition are not yet provided. The official job posting will be released soon, and interested candidates are encouraged to stay tuned for application instructions.

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.

The University of Wuppertal in Germany is offering a fully funded PhD position (Research Assistant) in the group of Prof. Dr. Peter Zaspel, starting March 1, 2026. This opportunity is part of the DFG Priority Programme “Molecular Machine Learning” and is embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes.” The project focuses on interdisciplinary research at the interface of computer science and mathematics, with a particular emphasis on developing bivariate molecular machine learning methods for modeling molecular interactions and properties. The successful candidate will contribute to the development of novel machine learning approaches, especially regression models for bi-molecular properties, and will work within the thematic context of multi-fidelity and active learning strategies for molecular systems. The research is highly collaborative, involving an international team and addressing questions in machine learning, uncertainty quantification, and high-performance computing, with applications spanning the natural and engineering sciences. In addition to research, the position includes teaching responsibilities (4 contact hours per week) and supervision of student research and thesis projects. The employment is governed by the German Academic Fixed-Term Contract Act (WissZeitVG) and supports doctoral qualification. The contract is full-time (part-time possible), initially limited to three years, with the possibility of extension to complete the doctorate. The salary is paid according to TV-L E13, providing competitive funding for the duration of the PhD. Applicants must hold a completed Master’s degree (or equivalent) in computer science, mathematics, physics, or data science. Strong analytical skills in machine learning and/or numerical mathematics are essential, as is proficiency in programming languages such as Python or C/C++. Experience with multipole methods, low-rank approximations, or tensor methods is desirable. A good command of English is required, as it is the working language of the team. The selection process includes a scientific programming task relevant to the advertised position, details of which can be found at this link . To apply, candidates must submit a motivation letter, CV, proof of graduation, relevant certificates or references, and (if available) a Bachelor’s or Master’s thesis, along with the completed scientific programming task. Applications are accepted via the University of Wuppertal’s online portal ( application portal ) under reference number 25353. The application deadline is January 19, 2026. For further information, contact Prof. Dr. Peter Zaspel at [email protected] or visit his academic page . This position is ideal for candidates interested in interdisciplinary research, machine learning, and molecular modeling, and who are motivated to contribute to cutting-edge scientific advancements in a collaborative international environment.

The University of Wuppertal in Germany is offering a fully funded PhD position (Research Assistant) in the group of Prof. Dr. Peter Zaspel, starting March 1, 2026. This opportunity is part of the DFG-funded ICEBAY research project, which aims to advance Bayesian hierarchical modeling and probabilistic inference for climate reconstruction by integrating borehole thermometry and ice-core data. The project is highly interdisciplinary, bridging computer science and mathematics, and focuses on solving complex, coupled inverse problems with explicit uncertainty quantification. The successful candidate will develop and apply advanced probabilistic inference methods and machine learning techniques to model uncertainty and integrate heterogeneous climate data. The research will contribute to climate reconstruction and involve collaboration with an international team working on probabilistic modeling, uncertainty quantification, and high-performance computing, with applications spanning the natural and engineering sciences. The position also includes teaching duties (one semester hour per week) and supervision of term papers and theses. Applicants should hold a completed Master’s degree (or equivalent) in computer science, mathematics, physics, or data science. Essential qualifications include strong analytical skills in statistics, machine learning, and/or numerical mathematics, as well as proficiency in programming languages such as Python or C/C++. Experience in Bayesian inference or hierarchical modeling is highly desirable. A good command of English is required, as it is the working language of the team. The ideal candidate will be proactive, motivated, able to work independently, and enjoy teaching. Employment is governed by the German Academic Fixed-Term Contract Act (WissZeitVG) and supports doctoral qualification. The position is full-time (part-time possible), initially limited to three years, with the possibility of extension for completion of the doctorate. Salary is paid according to TV-L E13. As part of the application process, candidates must complete a scientific programming task relevant to Bayesian inference for climate reconstruction ( see details ). To apply, submit a motivation letter, CV, proof of graduation, relevant certificates or references, and (if available) your Bachelor’s or Master’s thesis, along with the completed programming task. Applications should be submitted via the University of Wuppertal’s online portal ( link ) under reference number 25354 by January 19, 2026. For further information, contact Prof. Dr. Peter Zaspel at [email protected] . Additional details about the position and application process can be found on the DAAD portal .