A wide range of Computational Sustainability applications, including biodegradation pathway prediction and sustainable drug development, rely on experimental data generated with analytical techniques such as mass spectrometry (MS). While mature tools for standard MS data analysis already exists, as new MS technologies are rapidly developed to cope with the complexity of samples emerging in environmental or pharmaceutical sciences, these fail to exploit the full data potential offered by the new technologies. New computational methods are therefore needed and ML algorithms, would be particular valuable to cope with the complexity, noise and volume of MS data. The outcome will be new and computationally sustainable methods that will support the process of finding new anti-cancer drugs and other applications relying on the analysis of MS data.

Duration and Type

  • Preferable 1 semester, but can be also done in 2 semesters
  • Honours project, other postgraduate project (MSc, MProfStuds, …)


  • Basic maths, CS, and machine learning skills
  • Programming in Java or Python

Supervisor and contact

  • Joerg Wicker and Katerina Taskova
  • Send CV and transcript by mail to Joerg Wicker