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species Perform bioinformatics and computational analyses to identify microbiome-lifespan relationships Apply rigorous molecular biology methodologies to elucidate mechanisms underlying microbiome effects
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, the informatics department of the University of Kiel, and the remote sensing company EOMAP GmbH & Co. KG. Who are we? The IOW is an independent research institute of the Leibniz Association for which equal
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-for-computational-biomedicine ). Job-ID: V000013739 Field of application: Institute for Computational Biomedicine Location: Heidelberg Start date: 01.04.2025 or as soon as possible Job Category: Science and teaching
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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, Computational Biology, or a related field Proven experience in large-scale omics data analysis, preferably spanning multiple omics domains (e.g. methylation profiling, DNA/RNA sequencing, proteomics, single-cell
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2 Feb 2025 Job Information Organisation/Company Deutsches Krebsforschungszentrum Research Field Medical sciences Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country
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and Ethics in Medicine, we advance ethical practice and theory in medicine, biomedical technology, and public health, driven by the belief that embedding ethics is essential for shaping the future
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 2 months ago
will involve a combination of approaches based on molecular and computational biology and will be tailored to the specific interests of the successful candidate. Your profile You have a PhD or equivalent
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The Center for Systems Biology Dresden (CSBD) is seeking applications for ELBE postdoctoral fellows The ELBE postdoctoral fellows program welcomes independent researchers on the postdoctoral level
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science, and applied plant research Example reading: Peleke, F. F., Zumkeller, S. M., Gültas, M., Schmitt, A., & Szymański, J. (2024). Deep learning the cis-regulatory code for gene expression in selected