PhD position at RECETOX
3 days ago
Přehled
PhD position at RECETOX, Masaryk University (Brno, Czech Republic)
Computational Mass Spectrometry with Applications in Metabolomics and Exposomics
About RECETOX
RECETOX is an interdisciplinary centre dedicated to research and education in Environment & Health. Our work spans the study of toxic compounds, their environmental behaviour, transport, and bioaccumulation, with the aim of assessing human health risks and developing technologies to mitigate them.
About the Research Group
The Human Exposome research group, led by Dr Elliott J. Price and Dr Helge Hecht, investigates the profiling of chemical exposures and their links to population health. The group develops analytical and computational workflows for chemical exposomics and metabolomics, with a strong emphasis on creating and maintaining open-source software and training resources.
Scientific Context
Humans are continuously exposed to a complex mixture of chemicals from diet, lifestyle, pharmaceuticals, and environmental pollution. To understand how these exposures affect health, non-targeted analysis (NTA) by mass spectrometry (MS) is a key approach, capable of detecting thousands of compounds in biological and environmental samples. However, these datasets are complex and require advanced computational solutions for accurate and efficient analysis.
Possible Research Themes
We seek motivated PhD candidates to advance computational mass spectrometry in one or more of the following directions:
Algorithm Development
Design and improve computational methods for the pre-processing of MS data, exploring new algorithmic approaches for signal detection, deconvolution, and feature extraction.
Machine Learning for Chemical Characterisation
Apply and refine ML techniques for in silico annotation, prediction of physicochemical properties, and prioritisation of chemicals by toxicity or biological relevance.
Integrative Analysis of Large-Scale MS Datasets
Develop workflow ensembles that systematically combine complementary methods, enabling benchmarking, optimisation, and improved reproducibility.
Candidate Profile
The ideal candidate will have a background in computational sciences or life sciences with a strong interest in applying these skills to environmental health research. Experience with programming, statistics, or machine learning is an advantage.
Please send your application via email to and
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