Health research uses metabolomics, lipidomics, and similar methods to study the abundances of small molecular compounds in bodily fluids, tissue samples, breath, etc. Untargeted experiments measure hundreds of molecular abundances in single experiments, of which the chemical identities cannot always be fully established. Not all usage of the resulting data requires full identification, though knowing the identity makes it a lot easier to connect the experimental results to existing knowledge. One could for instance use identified compounds to understand the activity of individual metabolic routes. This would enable us to make better use of the biological pathways like they are described in WikiPathways.
This project will focus on improved analysis in ongoing research projects using as much available information as possible. If a broader category of compounds is found in an experiment or is described in a knowledge resource (e.g. a class of lipids or unidentified stereoisomers) coupling of analysis to what we know can still be possible. Instead, you will develop new methods and new tools and will combine available data in new ways to accommodate for incomplete experimental characterization, partly known chemical identity, and uncertainty in those. We will use top notch chemical and biological ontologies, identifier mapping databases, knowledge about food and body constituents, and novel methods to calculate pathway enrichment, meaningful biological networks, approaches to combine genetic traits and environmental aspects, and new visualization solutions. All this will be developed in the context of ongoing research in human metabolism and will be used to understand metabolism and metabolic diseases.
We seek a candidate with strong academic abilities and the ambition to become an excellent researcher. You have completed a research master in the natural (biological, chemical, biomedical) sciences, or an equivalent 2-year degree. Outstanding students with a 1-year regular master can be accepted in exceptional cases when their profile exactly matches the requirements for this research project.
We are looking for a prospective PhD candidate with either a background in bioinformatics or cheminformatics (or equivalent) with affinity for data integration issues (e.g. ontologies) and quantitative methods (e.g. regression and multivariate statistics methods); or a background in the biochemistry and of metabolic diseases. The ideal candidate will have covered both aspects, but candidates with experience in one of these and an interest in the other are encouraged to apply.
Furthermore the successful candidate should have:
- A proven interest and demonstrable ability to write computer code and organise data;
- Experience with source code development (version control, issue trackers, documentation) is preferred;
- Excellent writing skills in English;
- Strong demonstrable analytical skills;
- Affinity for working in an interdisciplinary and highly international environment;
- Good organisational skills;
- Willingness to relocate to (the vicinity of) Maastricht.
Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 16,000 students and 4,000 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Humanities and Sciences, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience. The research will take place at the Dept of Bioinformatics - BiGCaT, part of the NUTRIM research school.