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To qualify for the fellowship, the candidate should hold a PhD degree in one or more of the following fields: computational biology, systems biology, microbiology, bioinformatics, or related areas. The ideal
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at relevant international conferences;Contribute to grant writing and research proposals, as well as participate in other projects where the senior researchers are involved;Mentor graduate students (MSc and PhD
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responsibilities include bioinformatics analyses with a strong focus on single-cell RNA sequencing and next-generation single-cell barcode lineage tracing, as well as the interpretation of complex biological
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to illness, parental leave, clinical service, trade union duties or similar circumstances. The PhD degree should be in a computational or life science field, such as bioinformatics, ecology, mathematics
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meetings For further information, please contact Assoc. Prof. Carole Linster (email address: ). Your profile PhD degree in molecular biology, cell biology, biochemistry, genetics, or another similar life
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the following areas as demonstrated through at least one first-author publication: computational biology/bioinformatics, cheminformatics, analytical chemistry/mass spectrometry/metabolomics, or machine
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experience and enhanced potential to receive an ERC Starting Grant in the future. Open to both PhD (natural sciences) and MD (medical sciences) holders. From a variety of academic backgrounds: molecular
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on or as soon possible, with possibilities to extend for another two years. Job description You should be experienced in biochemistry, tissue proteomics, and protein bioinformatics. You will spearhead
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; Collaborating closely with the ENGREENIT’s PhD candidate (starting 12 months later than the PostDoc researcher), supervised by the Assoc. Prof. Emil Draževic, and will jointly develop the heterogeneous
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collaborating on a number of spatial transcriptomics projects unrelated to cancer and you will be taking part in building a strong local community focusing on the bioinformatics aspects of these types of data