Our lab 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 a talented and motivated postdoctoral fellow to join our team. The position is available immediately.
The successful candidate will be applying metabolomics and molecular biology techniques to discover unique plant natural products and characterize their biosynthetic genes. The IOCB 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.
Requirements
- PhD degree in the field of metabolomics, biochemistry, or molecular biology obtained no more than 5 years ago.
- Hands-on experience with high-resolution LC-MS, familiarity with metabolomics data processing. Experience with molecular networking is a plus.
- Good track record of previous scientific publications and achievements.
- Good proficiency in spoken and written English
- Passion for basic research and science.
We offer
- Competitive salary
- 5 weeks of vacation
- Health insurance and full benefits package incl. contribution to pension, culture, and sport
- Lunch subsidy
- Other IOCB employee benefits
How to apply
Please email your CV with a list of publications, a cover letter or a research statement, and contact details of 2-3 references to Dr. Tomáš Pluskal: tomas.pluskal@uochb.cas.cz.
Relevant publications
- Pluskal T, Torrens-Spence MP, Fallon TR, De Abreu A, Shi CH, Weng JK. The biosynthetic origin of psychoactive kavalactones in kava. Nature Plants 5:867
- Pluskal T, Weng JK. Natural product modulators of human sensations and mood: molecular mechanisms and therapeutic potential. Chemical Society Reviews 47:1592
- 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
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