Research Assistant (Doctoral student) project "Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes"
Bergische Universität Wuppertal

Temporary

Full Time, Part Time

Apply by: 2026-03-02

Published: 2026-01-28

Wuppertal

I am interested

Research Assistant
(Doctoral student)

The 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.

The School of Mathematics and Natural Sciences, Professorship for Software in Data-intensive Applications, invites applications.

RESPONSIBILITIES AND DUTIES

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 “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes” of the DFG Priority Programme “Molecular Machine Learning”
Development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties.
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.
Teaching responsibilities (equivalent to 4 contact hours per week) and supervision of student research and thesis projects

PROFESSIONAL AND PERSONAL REQUIREMENTS

Completed academic university degree (Master’s or equivalent) in a relevant discipline (e.g., computer science, mathematics, physics, data science)
Strong analytical skills in the context of machine learning and/or (numerical) mathematics
Excellent knowledge of a programming language (preferably Python or C/C++)
Interest in developing novel bivariate methods in machine learning for molecular property prediction within a relevant interdisciplinary application
Ideally, experience with multipole methods, low-rank or tensor approximations
Good command of English (working language within the team, international collaboration)
A competent, proactive personality with commitment and motivation
Ability to work independently and enjoyment of teaching
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-bi-molecular-machine-learning/

This 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.

Start
as soon as possible

Duration
up to 3 years

Salary
E 13 TV-L

Time
Full time (Part-time employment is possible, please indicate in your application whether you would also or only be interested in part-time employment.)

Reference Code
25353

Contact person
Mr Peter Zaspel
zaspel@uni-wuppertal.de

Applications via
stellenausschreibungen.uni-wuppertal.de

Application deadline
02.03.2026

WE OFFER

Friendly working environment

Occupational health management and University Sports

Flexible working hours and hybrid working

Working in an international context

30 days of leave

Large offer of continuing education courses

Family-friendly working conditions

Company pension scheme

The 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.

Applications 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 – if available) as well as the mandatorycompletion 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-bi-molecular-machine-learning/. Kindly note that incomplete applications will not be considered.

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