BAM's eScience section offers a master thesis in computer science, machine learning, data science, or a comparable field. Alternatively, research projects can be carried out as paid student assistants.

At BAM, we address significant environmental and infrastructure challenges while also conducting basic research in materials and analytical sciences.

The eScience group is a young and international group of scientists. We develop statistics, machine learning, and software solutions in collaborative research projects with applied research groups at BAM or at other research institutes. Our projects cover a wide range of applications, and we offer ideal conditions for creative minds! As a member of the S.3 eScience group, you will work in a highly interdisciplinary team of scientists and benefit from the professional exchange on methods of statistics, machine learning, and data management.

You will have the freedom to choose your project and tailor it to your interests.

Your tasks

  • You develop new machine learning methods for predicting materials properties or for solving inverse problems (e.g., Graph Neural Networks, Transformers, Invertible Neural Networks)
  • You analyze large data sets of either simulated materials (e.g., DFT, materials project) or from experimental measurements (e.g. SAXS/WAXS measurements)
  • You develop required workflows for analyzing data or for creating data from simulations
  • You dive deep into applications and develop an overall understanding of the data or measurement technique

Your qualifications

  • Master student in computer science, machine learning, data science or a comparable field
  • Very good programming skills in at least one programming language (e.g., Python, C/C++, Haskell, Lisp, Prolog, Julia)
  • Theoretical knowledge of methods in machine learning or statistics
  • Experience with machine learning or statistics libraries (e.g., PyTorch, Scitkit-Learn, GNU-R)
  • Experience with analyzing and visualizing large data sets
  • Very good oral and written expression in English
  • Very good communication skills, willingness to cooperate and ability to work in an interdisciplinary and international team of scientists

Contakt

Questions about this position will be answered by Dr. Philipp Benner on the telephone number +49 30 8104-3647 or by e-mail at philipp.benner@bam.de.

further information