科学机器学习与高性能计算方向博士职位

PhD position in Scientific Machine Learning: Data science at scale and mixed precision solvers

31.07.2026, Academic staff

We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13 position with an application deadline of August 14, 2026.

About us Located in the prosperous capital of Bavaria and home to over 39000 students, the Technical University of Munich (TUM) is one of the world's top universities. It is committed to excellence in research and teaching, interdisciplinary education, and the active promotion of promising young scientists. TUM benefits from the healthy mix of companies and startups of all sizes headquartered in the region and is tightly connected to regional research hospitals. The university also forges strong links with companies and scientific institutions across the world. We are looking for motivated researchers to join the HDSC/MDSI Tandem Project "Data science at scale: Training neural models with mixed precision solvers." This is a joint project involving Prof. Dr. Michael Bader (TUM CIT, Hardware-aware algorithms for HPC) , Prof. Dr. Felix Dietrich (TUM CIT, Physics-enhanced Machine Learning) , and Prof. Dr. Hartwig Anzt (TUM CIT, Computational Mathematics) . Remuneration is 100% TVL E13 according to the German public sector rates. You have the opportunity to pursue a doctoral degree (Dr. rer. nat., i.e. the "German PhD"). The primary place of work will be at the TUM Campus Garching. Description Do you thrive on challenging research projects that bridge the gap between theoretical data science and large-scale practical application? We have an exciting opportunity for you to join our team working on Scientific Machine Learning (SciML). The project focuses on developing scalable HPC algorithms and mixed-precision solvers to train neural models on massive datasets that exceed the capabilities of single-node systems. A key objective is the development of efficient iterative algorithms for training networks based on random features, which can be applied to regression, classification, and the solution of time-dependent partial differential equations (PDEs), such as large-scale tsunami simulations. The project involves two doctoral researchers with complementary directions. A researcher in Heilbronn will focus on precision-emulation to overcome the gap between low-precision GPU hardware and the high-precision requirements of linear algebra solvers. The position in Heilbronn is already filled. This job description is for a position in Garching . Here, you will develop general-purpose, scalable linear solvers for training neural networks that represent solutions to PDEs . Your work will involve utilizing GPU code generators for matrix multiplication kernels and ensuring the software is usable in Python for downstream data analysis [2]. Collaboration is central to this role. You will work closely with researchers across both campuses (Garching and Heilbronn), participating in meetings and semesterly research visits, working between computational mathematics and physics-enhanced machine learning .

Requirements • Master in Informatics, Mathematics, or a related field (e.g. Computational Science/Engineering) • Strong knowledge of machine learning, Scientific Computing (particularly linear algebra and numerics), and High Performance Computing (HPC). • Proficiency in programming; experience with GPU architectures, C++, and Python is highly valued. • Excellent communication and collaboration skills in English. • Soft skills: analytical thinking, structured and organized work, and high intrinsic motivation. • Preferred: Experience with linear operators, eigenproblems, or the development of numerical solvers.

How to apply? Please apply through the registration form and mention in the free text "Application: MDSI/HDSC 2026", no later than Friday, August 14, 2026 EOD . Please attach the following (all in one PDF): • Up to one page of motivation: Why do you want to work on this project, pursue a PhD, in this field of research, in this team, at TUM? • CV including academic education, (optional) industry experience, and (optional) completed projects. • Transcript of records (completed courses and grades). • Contact details of a person for a letter of reference. We will not contact them unless the hiring decision is unclear.

Registration form: https://www.cs.cit.tum.de/scml/jobs-and-projects/

Note that TUM has been pursuing the strategic goal of substantially increasing the diversity of its staff. As an equal opportunity and affirmative action employer, TUM explicitly encourages nominations of and applications from women as well as from all others who would bring additional diversity dimensions to the university's research and teaching strategies. International candidates are highly encouraged to apply. The position is suitable for disabled candidates, and preference will be given to disabled candidates with equal qualifications.

Contact If you have any questions about the application or the project, write an email (see website):

Prof. Dr. Felix Dietrich The research group homepage: https://www.cs.cit.tum.de/scml Link to the personal page

The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

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When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: https://www.cs.cit.tum.de/en/sccs/people/felix-dietrich. --- noreply@tum.de