The University of Bonn is an international research university offering a broad range of subjects. With a 200-year history, some 31,500 students, more than 6,000 employees and an outstanding reputation in Germany and abroad, the University of Bonn is one of the leading universities in Germany and has been awarded the status of a University of Excellence.
The newly established Hertz Chair in “Astrochemistry” within the Transdisciplinary Research Area Matter: Building Blocks of Matter and Fundamental Interactions (TRA 2) is seeking a full-time, permanent
to start on October 1, 2026.
The field of Astrochemistry is an interdisciplinary field at the interface of Astronomy, Physics, chemistry and Computer Science. Prof Viti's group is at the forefront of astrochemical modelling. We develop innovative methodologies to connect theory and observations. This led over the years to the development and maintenance of an open-source suite of astrochemical and statistical/machine learning codes.
We seek an outstanding candidate who will take the lead on the application of computational methodologies for the group. This includes three core competencies: scientific software engineering, machine learning engineering and data science.
Potential areas of responsibility:
- Development and maintenance of the in-house Astrochemical codes,
- Machine learning and artificial intelligence applied to astronomical data,
- Advanced statistical and probabilistic inference methods,
- Scalable algorithms for the analysis of large observational, simulated, or laboratory data sets,
- Efficient use of high-performance computing (HPC) on platforms ranging from local university clusters, first tier national HPC facilities and cloud-based platforms,
Data management, archiving, and reproducible research practices.
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We welcome candidates whose expertise bridges astronomy and data science — whether through the development of new computational methods, the application of such techniques to cutting-edge data, or both.
Your tasks:
- Develop and maintain the in-house Astrochemical codes,
- Support the group in the exploitation and interpretation of large observational and computational datasets,
Research and keep up with the Data Science literature that could benefit the group.
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Requirements:
- A PhD in Astronomy, Astrophysics, Physics, Computer Science, or a closely related field or proven equivalent knowledge and experience,
- Python experience,
- Familiar with git, Github and Gitlab,
- Fortran experience,
- Experience with at least one machine learning framework (Tensorflow, Pytorch or Jax) is advantageous,
- Experience with CI/CD and test driven development,
- Experience maintaining an (open source) research code,
Enthusiasm for collaborative research within a University group.
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We offer:
- A varied and challenging role with one of the region’s largest employers, job security, and a commitment to the local community,
- Flexible working hours (including the option to work from home to a certain extent),
- An international work environment,
- Opportunities for continuing education and professional development,
- Occupational pension (VBL),
- Many options available for university sports,
- Excellent access by public transport thanks to Bonn’s central location as well as the opportunity to take advantage of low-cost parking,
Remuneration in accordance with TV-L pay grade 13.
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The University of Bonn promotes an open, respectful, and non-discriminatory academic culture and is actively committed to equal opportunity and family friendliness. We aim to eliminate structural disadvantages and to strengthen diversity in research, teaching, and administration. Therefore, we welcome all applications, regardless of ethnic or social background, gender, sexual orientation, religion or belief, disability, or age.
Applications from women and people with a documented severe disability (or equivalent status) will be given preferential consideration in accordance with the State Equality Act (LGG) NRW and Social Code (SGB) IX.
If you are interested in this position, please submit your complete and detailed application materials by 05.07.2026, citing reference number 2026/273, using the online application form . For further information, please contact Professor Viti ([email protected]).
