74 fully-funded-phd-program-computer-science-eth Postdoctoral positions at Nature Careers in Denmark
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The Computational Protein Engineering (CPE) group at The Novo Nordisk Foundation Biotechnology Research Institute for the Green Transiation (BRIGHT) is developing novel methods to engineer proteins
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matrix dynamics. The ideal candidate has the following competencies and skills: A PhD in biomedicine, or a related discipline such as biochemistry, molecular biology, medicine, cell biology, immunology
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Development Programme targeted at career development for postdocs at AU. You can read more about it here . At the Faculty of Natural Science at Aarhus University, we strive to support our scientific staff in
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structures. You will work closely with computational researchers to gather data, evaluate AI predictions, and design experiments. You will work in a team with 7 PhD-students and 4 postdoctoral researchers and
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Zheng. Your competences You have academic qualifications at PhD level, for example within computational biology, bioinformatics, spatial omics, or related areas. Experience with computational imaging
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to understand RNAs role in the onset of Darwinian evolution. The lab takes inspiration from simple natural replicons for engineering RNA systems that can restart replication [1-2]. The newly funded Circles
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advanced data science, using state-of-the-art human stem cell models to uncover previously unrecognized environmental risk factors for Parkinson’s Disease. You will, in close collaboration with a PhD student
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range from cell biological over biochemical to molecular biology and bioinformatics approaches. Collaborations with structural biologists are possible. Your profile Applicants should hold a PhD in
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Danish Center for Health Economics (DaCHE), Department of Public Health (IST), University of Southern Denmark (SDU) invites applications for a fully funded two-year postdoctoral position
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qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation models); Strong programming