28 algorithm-phd-"Prof"-"Prof" Postdoctoral positions at Aarhus University in Denmark
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of machine-learning algorithms for unmanned aerial vehicles; dissemination of the results in international conferences and journals; proposal writing for external funds. Your profile The successful candidate
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research environment with 2 postdoctoral fellows, 9 PhD students, 5-6 research year students and 4-7 master’s students. We are located next to the department of dermatology’s outpatient clinic and take
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participate in knowledge exchange with public authorities and industry and will be involved in teaching and supervising students at the BSc, MSc, and PhD levels. Qualifications for a postdoc position: Academic
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Denmark Region. Within rheumatology, we have 3 professors, a clinical chair professor, a professor of immunology and a professor of health services research, 4–5 postdoctoral fellows, 7-10 PhD students, 3–5
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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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tasks will consist of: Independent research of high international quality, including publication. Guidance and supervision of Bachelor’s and Master's degree students, as well as supervision of PhD
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contributes to developing the academic field in question. Your qualifications To qualify for the positions, you must have been awarded the degree of PhD or have acquired equivalent qualifications. Who we
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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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and across disciplines within the Pioneer Center Land-CRAFT Contribute to development of teaching and PhD supervision activities in the Dept. of Agroecology or other departments at AU with which we
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contribute to development of research grant applications. Your profile The applicants should hold a PhD in structural dynamics with focus on data-driven methods (e.g., for input/state/parameter estimation) and