In this project, we are using machine learning for extracting chemical information from spectral data. FTIR, Raman or surface-enhanced Raman scattering (SERS) techniques have the potential of unveiling hidden information from complex spectra of biochemical mixtures. As a result, from thousands of measurements, a large spectral data set is analyzed by machine learning methods. The methods proposed in this project will be used to detect disease-specific biomarkers at very low concentrations from bio-samples for the detection of Alzheimer’s disease. In parallel, machine learning will be also applied to differentiate between healthy and unhealthy samples from the acquisition of microscopy images.


Job description:

  • Increase the number of significant features in large data sets by systematic measurements with different spectroscopic techniques (Raman, FTIR, SERS, …).
  • Light microscopy and fluorescence microscopy image acquisition.
  • Further development of machine learning algorithms to reduce error rates in detection (MATLAB, Python or in R).
  • Identify additional machine learning models and strategies of classification/regression that fit to the main objective of the project.


Main outcome of the project:

  • Dataset from different spectroscopic techniques based on human samples from healthy and not diseased groups.
  • Optimized machine learning protocol for the detection of biomarkers from vibrational spectroscopy data.
  • Description of the capabilities, performance, and benefits of the method proposed.
  • A guideline for the implementation of machine learning algorithms applied to data from several spectroscopy techniques.


We encourage highly motivated candidates to carry out their final Master’s thesis in an international, multidisciplinary research environment. Preferably, the students should have the following background: Computer Science, Mathematics, Physics, Chemistry, Biomedical Engineering, etc. For more information, please visit our website



If you are a (prospective) master student and you are interested in this project, please get in touch with the scientist in charge: Andreas Seifert (

To apply for a master position fill in the form below and follow the instructions and recomendations of the general call (open until 30 June 2023).



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