Up to two open PhD positions in numerical methods for large-scale training of Gaussian processesFull PhD

  • English

    Working Language

  • Wuppertal


  • 15 Jul 2024

    Application Deadline

  • as soon as possible

    Starting Date


Open Positions


Time Span

as soon as possible for 3 years

Application Deadline

15 Jul 2024



Type of Position

Full PhD

Working Language


Required Degree


Areas of study

Information Technology, Applied Computer Science, Computer Science, Practical Computer Science, Applied Mathematics, Technomathematics, Statistics



Are you interested in developing new numerical methods for training of machine learning models on large to huge data sets? We are currently looking for PhD candidates that support us in breaking the barriers of computational complexity of Gaussian processes and kernel-based machine learning models.

Up to two PhD positions are available in the team of Prof. Peter Zaspel at University of Wuppertal, Germany. The positions are focused on the development of novel (numerical) methods with fast implementations that help to break the computational complexity in the training of Gaussian processes and kernel-based machine learning models. Depending on the candidate’s interest, a stronger focus can be put on the methods development in fast matrix approximations (e.g. hierarchical matrices, low-rank methods, sparse GPs, etc.) or on the fast implementation on GPUs, i.e. hardware-aware numerics and parallelization.

The team of Prof. Peter Zaspel is located at Bergische Universität Wuppertal. The international team focuses on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in the research group on Scientific Computing and High Performance Computing. For more details, see https://www.peter-zaspel.de/ and https://hpc.uni-wuppertal.de.

A successful applicant is expected to have a Master’s degree (or equivalent) in computer science, mathematics, physics, data science or similar discipline, strong analytical skills in context of machine learning and/or (numerical) mathematics, very good to excellent proficiency in a programming language (preferable Python or C/C++) and interest in developing novel training methods for kernel-based / Gaussian Process machine learning. Experience in matrix approximation techniques or hardware-aware programming on GPUs is an advantage. A good command of English is essential, both as the local working language and because of international collaborations. We look for a competent personality with initiative and commitment, who has the ability to work independently and who enjoys teaching (support).

We offer a 3 year PhD position. The salary will be paid in accordance with the Collective Agreement for the Public Service of the Federation (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), with salary level 13 (75%). The position has teaching (support) duties. The place of employment will be Wuppertal, Germany.

The positions are available immediately and applications will be considered on a rolling basis, but not later than until July 15, 2024. In order to apply, please submit a letter of motivation, a CV, copies of transcripts and optionally a copy of your MSc thesis (all as one PDF). If you you would like to apply or have questions on the position please contact Prof. Peter Zaspel via zaspel(at)uni-wuppertal.de.

Required Documents

Required Documents
  • Motivation letter
  • CV
  • Certificates
  • Transcripts


Legal notice: The information on this website is provided to the DAAD by third parties. Despite careful checking, the DAAD cannot guarantee the accuracy and completeness.
DAAD - Deutscher Akademischer Austauschdienst - German Academic Exchange Service