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Field
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a master's degree or equivalent in the field of genetic epidemiology, statistical genetics, medical data science, or bioinformatics. have academic training and practical experience in data science
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meet the following criteria: Essential Qualifications: Applicants for PhD must have a first-class or upper second-class undergraduate degree (or equivalent) in Neuroscience, Biochemistry, Bioinformatics
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Science, Bioinformatics, Epidemiology, or a closely-related area, or else a lower second-class degree followed by a relevant Master's degree. They must have a strong background in mathematical modelling and an interest in
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will remain open until filled. 2 available. 2 available. Applicants need to have a background in immunology, genetics or bioinformatics. They must have completed a relevant Bachelor’s Degree and Honour’s
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remain open until filled. Position will remain open until filled. 2 available. 2 available. Applicants need to have a background in immunology, genetics or bioinformatics. They must have completed a
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the research landscape of Göttingen. The excellent biological, ecosystemic, environmental and mathematical research in Göttingen is reflected in this PhD programme. Sub-programmes: bioinformatics (computational
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deepest branches of the tree of life with the help of bioinformatics and experimental molecular techniques. Research about how chemicals of anthropogenic origin interact with cellular functions and cause
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imaging and analysis methods together with an interdisciplinary team of physicists and structural biologists. Your Profile: Master degree in biophysics, bioinformatics, molecular or cell biology or related
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, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery
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(genome sequencing), bioinformatics and single-cell (microscopy, microfluidics, FACS) analyses of E. coli bacteria and will be centred around the following themes: Pathways involved in robustness against de