Postdoc in Systems Biology, Computational Biology, Bioinformatics or Computer Science: Germany

The Cluster of Excellence “Balance of the Microverse” at the Friedrich Schiller University Jena, Germany, combines expertise in life, material, optical and computational sciences to elevate microbiome studies from descriptive to hypothesis-driven and functional analyses. Our core mission is to elucidate fundamental principles of the interactions and functions in microbial communities in diverse habitats ranging from oceans and groundwater to plant and human hosts. We aim to identify the shared characteristics of disturbed or polluted ecosystems as well as infectious diseases on the microbiome level, and develop strategies for their remediation by targeted interventions. Our full spectrum of expertise in the physical and life sciences will be leveraged to address these important issues in natural habitats as well as synthetic arenas in a collaborative manner. The affiliated early career program of the Jena School for Microbial Communication (JSMC) offers an ambitious, structured and interdisciplinary post-graduate training based on top-level fundamental research.

The research group of Prof. Dr. Gianni Panagiotou at the Cluster of Excellence Balance of the Microverse invites applications for a Postdoctoral Position (m/f/d) to conduct research on the project “Identification of universal mediators of microbiome community structure through large scale cross-habitat comparisons” commencing on 01.10.2022. A later start may be possible if desired. The position is initially limited to 2 years.

Short abstract of the project

An exciting new frontier in microbiome research is the rational editing of disbalanced microbial communities. However, the selection of species to be either introduced directly or serve as chassis strains for modifying the structure and function of a dysbiotic microbiome is challenging since many criteria must be fulfilled. In most cases the selection of promising species is based on the ability to cultivate and genetically modify them and their known phenotypic properties, however, their ecological properties are rarely considered. Towards this direction, we plan to develop artificial intelligence workflows using large-scale community sequencing data sets from host, soil, and aquatic environments to shed light on the ecological properties of the different genera and on the potential of using them as global or habitat-specific modifiers of microbial communities. Therefore, the successful candidate is expected to elucidate factors that determine microbial colonization and persistence, as well as, microbial interdependencies within complex communities.

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