Undergraduate Assistant (m/f/d) in the field of study Computer Science, Statistics, Machine Learning or comparable



Reference number


Employment category

Part-time / 40 Monatsstunden

Preferred start date



Payment for students (payed per hour)

Contract Term

Limited / 6 Monate


Berlin Steglitz

Unter den Eichen 87
12205 Berlin

Section S.3 - eScience

To strengthen our team in the section “eScience” in Berlin-Steglitz, at the earliest possible date, we are looking for an

Undergraduate Assistant (m/f/d) in the field of study Computer Science, Statistics, Machine Learning or comparable

35 - 40 hours/month
Temporary contract for 6 months
Hourly wage 12.68 Euro

The Bundesanstalt für Materialforschung und -prüfung (BAM) is a materials research organization in Germany. Our mission is to ensure safety in technology and chemistry. We perform research and testing in materials science, materials engineering and chemistry to improve the safety of products and processes. At BAM we do research that matters. Our work covers a broad array of topics in the focus areas of energy, infrastructure, environment, materials, and analytical sciences.

The eScience team develops statistical and machine learning methods in collaboration with other departments at BAM. In this project, you will develop methods for the automated processing and analysis of small-angle X-ray scattering data (SAXS). The five-meter-long "Methodology Optimization for Ultrafine Structure Exploration"-instrument, or MOUSE for short, can quantify nanostructures over an unprecedented wide range from 0.2 to 2000 nanometers in large sample volumes. New data correction and evaluation methods developed at BAM complete the instrument and are essential to reaching its full potential. Machine learning methods need to be developed to address the current measurement methodology bottleneck: allowing for automated classification of large numbers of experimental measurements. Success in this project will lead to adoption of the ML methods to other techniques as well.

We are looking for talented people to join us.

Your responsibilities include:

  • Preparation of data sets using data augmentation techniques or simulations
  • Development and testing of machine learning models to classify and tag measurements
  • Integration of developed methods into existing pipelines
  • Documentation of source code
  • Supporting further tasks in the field of data science

Your qualifications:

  • Student of computer science, machine learning, or comparable subject, preferably with a bachelor's degree
  • Experience with machine learning methods and image analysis
  • Very good knowledge of relevant programming languages including Python (and preferably its machine learning libraries)
  • Experience with contributing to collaborative software projects using version control systems (e.g., Git)
  • Interest in physical sciences using specialized, one-off equipment
  • Good knowledge of English
  • Ability to work, organize and structure tasks independently
  • Willingness to occasionally attend intercontinental virtual meetings in the evening

We offer:

  • Interdisciplinary research at the interface of politics, economics and society
  • Work in national and international networks with universities, research institutes and industrial companies
  • Outstanding facilities and infrastructure
  • Flexible working hours, mobile working

You are enrolled at a German university for the period of employment.
The maximum working time with a part-time job is 80 monthly hours.

Your application:

We welcome applications via the online application form by 11.04.2021. Alternatively, you can also send your application by post, quoting the reference number 76/21-S.3 to:

Bundesanstalt für Materialforschung und -prüfung
Referat Z.3 - Personal
Unter den Eichen 87
12205 Berlin

Dr Philipp Benner will be glad to answer any specific questions you may have. Please get in touch via the telephone number +49 30 8104-3647 and/or by email to philipp.benner@bam.de.

BAM pursues the goal of professional equality between women and men. We therefore particularly welcome applications from women. In addition, BAM supports the integration of severely disabled persons and therefore expressly welcomes their applications. With regard to the fulfilment of the job advertisement requirements, the application documents are examined individually. Recognised severely disabled persons will be given preferential consideration if they are equally suitable.

The advertised position requires a low level of physical aptitude.