Interdisciplinary research at the interface of computer science and mathematics applied to climate reconstruction; development and application of probabilistic inference and machine learning methods for uncertainty modeling and heterogeneous climate data integration; collaboration on international research topics in machine learning, uncertainty quantification, and high-performance computing; teaching (1 semester hour per week) and supervision of student papers and theses
Requirements:
Completed master’s degree or equivalent in computer science, mathematics, physics, or data science; strong analytical skills in statistics, machine learning, or numerical mathematics; excellent programming skills (preferably Python or C/C ); interest in interdisciplinary modeling and solving inverse problems; ideally, experience in Bayesian inference or hierarchical modeling; good command of English; competence, proactivity, commitment, and motivation; ability to work independently and enjoy teaching; successful completion of a scientific programming task related to the position
Text:
:"# Research Assistant (Doctoral student)nnThe University of Wuppertal is a dynamic, networked and research-oriented campus university. Collectively, more than 25,000 researchers, academic staff and students face the challenges of science, education, culture, economics, society, technology and the environment.nnThe School of Mathematics and Natural Sciences, Professorship for Software in Data-intensive Applications, invites applications.nn## RESPONSIBILITIES AND DUTIESnn- Interdisciplinary work at the interface of computer science and mathematics with applications in the context of climate reconstruction within the DFG-funded research project u201cICEBAY u2013 Temperature reconstruction combining boreholes thermometry and ice-cores with Bayesian hierarchical modeling.u201dn- Development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate datan- Collaboration in an international team on related research topics in machine learning, uncertainty quantification, and high-performance computing with applications in the natural and engineering sciencesn- Teaching (1 semester hour per week) as well as supervision of term papers and thesesnn## PROFESSIONAL AND PERSONAL REQUIREMENTSnn- Completed degree (master or equivalent from university or university of applied sciences) in a relevant discipline (e.g., Computer Science, Mathematics, Physics, Data Science)n- Strong analytical skills related to statistics, machine learning, and/or (numerical) mathematicsn- Excellent command of a programming language (preferably Python or C/C )n- Interest in modeling and solving a complex, coupled inverse problem in a relevant interdisciplinary applicationn- Ideally, experience in Bayesian inference or Bayesian hierarchical modelingn- Good command of English (working language within the team, international collaboration)n- A competent, proactive personality with commitment and motivationn- Ability to work independently and enjoyment of teachingn- Successful completion of a scientific programming task in the subject area of the advertised position. All details of the programming task can be found at: [https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bayesian-inference-for-climate-reconstruction](https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bayesian-inference-for-climate-reconstruction)nnThis is a qualification position in the sense of the Academic Fixed-Term Contract Act (WissZeitVG), which serves to support a doctoral programme. The position is temporary for the duration of the doctoral process, but initially up to 3 years. An extension for the completion of the doctorate is possible within the time limits of the WissZeitVG.nnStart as soon as possiblennDuration up to 3 yearsnnSalary E 13 TV-Lnn## TimennFull time (Part-time employment is possible, please indicate in your application whether you would also or only be interested in part-time employment.)nnReference Code 25354nnContact person Mr Peter Zaspel [zaspel@uni-wuppertal.de](mailto:zaspel@uni-wuppertal.de)nnApplications via [stellenausschreibungen.uniwuppertal.de](https://stellenausschreibungen.uniwuppertal.de)nnApplication deadline 02.03.2026nnnn## WE OFFERnn- Friendly working environmentn- Occupational health management and University Sportsn- Flexible working hours and hybrid workingn- Working in an international contextn- 30 days of leaven- Large offer of continuing education coursesn- Family-friendly working conditionsn- Company pension schemennThe University of Wuppertal is an equal opportunity employer. Applications from persons of any gender and persons with disabilities as well as persons with an equivalent status are highly welcome. In accordance with the Gender Equality Act of North Rhine-Westphalia, women will be given preferential consideration unless there are compelling reasons in favour of an applicant who is not female. The same applies to applications from disabled persons, who will be given preference in the case of equal suitability.nnApplications including all relevant credentials (motivation letter, CV, proof of successful graduation, job references, and if applicable, evidence of a severe disability, ideally Bachelor/Master thesis u2013 if available) as well as the mandatory completion of a scientific programming task related to the thematic context of the advertised position. All details regarding the programming task can be found at: [https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bayesianinference-for-climate-reconstruction](https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bayesianinference-for-climate-reconstruction). Kindly note that incomplete applications will not be considered.",
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