Interdisciplinary work at the interface of computer science and mathematics on molecular machine learning in an international project; development of novel machine learning methods for modeling molecular properties; collaboration on research in machine learning, uncertainty quantification, and high-performance computing; teaching responsibilities (4 contact hours per week) and supervision of student research and thesis projects
Requirements:
Completed master's or equivalent in computer science, mathematics, physics or data science; strong analytical skills related to machine learning and/or numerical mathematics; excellent programming skills (preferably Python or C/C ); interest in developing novel bivariate machine learning methods for molecular property prediction; ideally experience with multipole methods, low-rank or tensor approximations; good command of English; proactive and motivated personality; ability to work independently and enjoyment of 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 molecular machine learning, within the thematic scope of the project u201cMulti-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexesu201d of the DFG Priority Programme u201cMolecular Machine Learningu201d.n- Development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties.n- Collaboration in an international team working on related research questions in machine learning, uncertainty quantification, and high-performance computing with applications in the natural and engineering sciences.n- Teaching responsibilities (equivalent to 4 contact hours per week) and supervision of student research and thesis projects.nn## Professional and Personal Requirementsnn- Completed academic university degree (Masteru2019s or equivalent) in a relevant discipline (e.g., computer science, mathematics, physics, data science).n- Strong analytical skills in the context of machine learning and/or (numerical) mathematics.n- Excellent knowledge of a programming language (preferably Python or C/C ).n- Interest in developing novel bivariate methods in machine learning for molecular property prediction within a relevant interdisciplinary application.n- Ideally, experience with multipole methods, low-rank or tensor approximations.n- Good command of English (working language within the team, international collaboration).n- A competent, proactive personality with commitment and motivation.n- Ability to work independently and enjoyment of teaching.n- Successful completion of a scientific programming task within the thematic context of the advertised position. Full details on the programming task can be found at [https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bimolecular-machine-learning/](https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bimolecular-machine-learning/).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 25353nnContact person Mr Prof. Dr. Peter Zaspel [zaspel@uni-wuppertal.de](mailto:zaspel@uni-wuppertal.de)nnApplications viannstellenausschreibungen.uniwuppertal.dennApplication deadline 02.03.2026nn---nn## 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-bimolecular-machine-learning/](https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bimolecular-machine-learning/). Kindly note that incomplete applications will not be considered.",
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