Integration of computational mass spectrometry and risk-based datamining in food safety analysis

Wageningen University
Bioinformatics Group


High-resolution mass spectrometry generates extensive data sets contain much more information than we can currently extract from them. Complementary to looking for single chemical compounds, identifying patterns in mass spectra that can be associated with chemical risks might give additional insights about potential hazards in our food and feed. To do so, the candidate will set up a computational mass spectrometry workflow to find yet unknown food safety hazards in high-resolution mass spectrometry data. In addition, using toxicity prediction tools and sample meta-data, the most relevant food toxicants will be filtered from 1000s of annotated compounds. Various new, exciting high end mass spectrometers are available to obtain part of the input data. Tasks include:
- setting up a pipeline to structurally annotate high-resolution mass spectrometry data
- designing a toxicity prediction tool based on chemical structure databases
- developing a toxicity-based data mining workflow based on both spectral and structure input
- applying the entire workflow to a real-life analytical data set for food safety


Candidates should hold a successfully completed MSc in (bio)informatics, metabolomics, (bio)analytical chemistry or related area, with a strong interest and demonstrable skills in programming (in Python), (computational) metabolomics, and/or analytical chemistry, preferably high-resolution mass spectrometry. We are specifically seeking candidate with excellent communication skills, both oral and written, who like to work in a multidisciplinary setting, and are critical, accurate and efficient way.

Start date

As soon as possible

How to Apply

Please fill out the application form on our website, Applications are dealt with in order, so please do not defer submission.


Justin van der Hooft