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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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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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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
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of personalized solutions we offer. Welcome to the UKE. PhD position / Postdoc position (all genders) in AI for biomedical data analysis Part time | Temporary | Hamburg Eppendorf UKE_Zentrum für Molekulare
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these processes, with the aim of understanding how ageing promotes CH progression. You will hold a PhD within the area of molecular biology or have thesis submitted at time of application, and will have experience
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interpretation of OMICs datasets (DNA, RNA, proteome) Supervision and technical guidance of Bachelor’s, Master’s, and PhD students, as well as technical staff within the project context Participation in teaching