The laboratory Biochemistry of Plant Specialized Metabolites led by Tomáš Pluskal at IOCB Prague combines cutting-edge experimental (e.g., LC-MS, metabolomics, RNA-seq) and computational (e.g., bioinformatics, molecular networking, machine learning) approaches to develop rapid, generally applicable workflows for the discovery and utilization of bioactive molecules derived from plants. We are looking for talented and motivated computational researchers to join our team.

The successful candidate for this position will be developing the next generation of the MZmine platform for mass spectrometry data processing in metabolomics. Among other things, we are aiming to add full support for ion mobility spectroscopy (IMS) to MZmine, and to enhance its molecular networking capabilities.

The IOCB Prague is one of the leading research institutes in central Europe with a vibrant international environment and excellent equipment and core facilities located in modern buildings close to the Prague city center.


  • MSc or PhD degree in the field of computer science, bioinformatics, or machine learning
  • Experience with Java programming
  • Good proficiency in spoken and written English
  • Passion for basic research and science


  • Competitive salary
  • Five weeks of vacation
  • Health insurance and full benefits package incl. contribution to pension, culture, and sport; lunch subsidy


Please email your CV, a cover letter, and contact details of 2-3 references to Dr. Tomáš Pluskal: tomas.pluskal@uochb.cas.cz.


  • Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11:395 (2010)
  • Pluskal T, et al. Metabolomics Data Analysis Using MZmine. in Processing Metabolomics and Proteomics Data with Open Software: A Practical Guide (ed. Winkler, R.) 232–254 (The Royal Society of Chemistry, 2020)
  • Nothias LF, et al. Feature-based molecular networking in the GNPS analysis environment. Nature Methods 17:905 (2020)