44 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Aarhus University in Denmark
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scientific journals. Your profile The position is offered to a highly motivated candidate with a PhD degree in Soil Science, Environmental Science, Biogeochemistry, Wetland Science, or related disciplines. We
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. The overall goal of the project is to design a novel high temperature heat pump. You will be conducting Computational Fluid Dynamic (CFD) simulations and cycle analysis, which will be instrumental
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Are you interested in X-ray imaging and biomineralization and in contributing to an international interdisciplinary Human Frontier Science Program funded project on skeletal adaptations in
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Anatolia and from the Tigris to the Mediterranean with a focus on visual culture. Qualifications Applicants must hold a PhD degree in Classical Archaeology or closely related disciplines and be able
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is important that you are able to work in a team and work for the overall goal in the project. Your profile The applicant should have demonstrated excellence and have a relevant PhD degree in chemical
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and PhD students working on various aspects of computational biology and hosts a number of collaborations with the Hospital. BiRC hosts the genomeDK supercomputer, which will be available
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research and teaching environment and activities. We expect you to teach and supervise students at Bachelor’s and Master’s level and to carry out research of the highest international standards, which
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are enrolled in our study programs. Furthermore, we also offer an ambitious PhD program. Our PhD students have high academic ambitions and deliver high-quality results for both the private and the public sectors
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directed to Associate Professor Helle Strandgaard Jensen (hs.jensen@cas.au.dk ) Qualifications A PhD in history, cultural studies, anthropology, sociology, media studies or related areas focusing on modern
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the sea, combining rich cultural life with easy access to nature. Qualifications PhD in mathematics (completed or expected before start date). Preferred research record in probability theory, analysis