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Field
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within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or a
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researcher interested in studying human-associated microbial communities in health and disease. The ideal candidate has the following qualifications: A PhD degree in bioinformatics, computational biology
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postdoctoral researcher interested in studying human-associated microbial communities in health and disease. The ideal candidate has the following qualifications: A PhD degree in bioinformatics, computational
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expected to be bioinformatic in nature. Applicants must have a PhD in a relevant subject, experience of macrosynteny analysis and phylogenetic analysis of gene families, and a thorough knowledge of animal
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that control the response to low oxygen conditions in Marchantia polymorpha. They will contribute both to the practical work with plants but also some bioinformatics work on protein structure and function
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, crystallography, study of biomolecular interactions, genomics and proteomics is available. The department has access to powerful computational capacities used for computational chemistry, bioinformatics and
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for differential rate correction). The successful applicant will hold, or be close to completion of, a relevant PhD/DPhil in bioinformatics, comparative genomics and phylogenetics, together with relevant experience
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records, give presentations, and prepare reports and publications. Required Qualifications PhD (or analogue) and substantial knowledge of human and/or animal reproductive physiology. Significant experience
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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interest in infectious disease epidemiology are essential for the post. For appointment to Research Associate, you should hold a PhD in infectious disease epidemiology, evolutionary biology, bioinformatics