Research Associate (m/f/d)
Institute for
Remuneration EG 13
ID 15926WE10
Start of employment as soon as possible
Application deadline 17.07.2026
Scope fulltime and fixed term until 31.08.2030
As part of the Institute of Mathematics at TU Hamburg, the Chair Computational Mathematics specializes in developing, implementing, and analyzing efficient numerical algorithms for differential equations. Our work spans a diverse application spectrum - from geophysical fluid dynamics to chemical process engineering - with a strong focus on numerical algorithms suitable for high-performance computing systems and scientific machine learning, combining machine learning with traditional numerics.
We are seeking a researcher to advance our work in parallel-in-time integration algorithms and drive forward their deployment on state-of-the-art parallel computers and GPUs. In this role, you will build upon our established theoretical and practical foundation in parallel spectral deferred corrections. Your ultimate goal will be to pioneer conclusive demonstrations of speedup for practically relevant applications on extreme-scale HPC systems like JUPITER at Jülich Supercomputing Centre.
YOUR TASKS
- To develop, test and analyse new numerical algorithms for differential equations
- To present your results at scientific conferences
- To publish your results in the form of peer-reviewed scientific publications
- To teach exercise groups for students in engineering programs (approximately 4 contact hours per week during teaching time)
YOUR PROFILE
Requirements
- Completed scientific university studies (masters degree or equivalent), in particular in the subject Applied Mathematics, Computational Mathematics or Computational Science
- Excellent knowledge of numerical mathematics, ideally with a focus on numerical methods for differential equations
- Experience with programming in a language like Python, Matlab, Julia, C/C++, Fortran
- Experience with running and benchmarking code on high-performance computing systems
- Fluent in spoken and written English
- Experience in other relevant areas like scientific machine learning or inverse problems etc is considered a plus
- Close supervision and strong support for PhD completion
- A job in an interesting, friendly and appreciative working environment
- Flexible and family-friendly working conditions
- Possibility of mobile working
- Internal offers for health promotion and special conditions in gyms
We value diversity, therefore all applications are welcome, regardless of gender, gender identity, ethnic origin, nationality, age, religion and belief, disability, sexual orientation and identity or social background.
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Please send your complete application documents (cover letter, curriculum vitae in table form, proof of completed training and/or university degree, job references or certificates of employment) via the online application system.
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