33 digital-image-processing-phd-scholarship Postdoctoral positions at Aarhus University in Denmark
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design and scaling) Qualifications/ About you Qualified individuals should have a PhD in, preferentially anaerobic, microbial sciences, microbial electrochemistry, or related bioengineering disciplines
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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a final report. Active collaboration with project partners is also expected. Your profile Applicants should hold a PhD in mechanical engineering at the time of starting the position. The selected
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research center at Aarhus University and the Royal Academy of Music, Aarhus/Aalborg, Denmark (RAMA) aiming to address the dual questions of how music is processed in the brain and how this can inform our
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applicants regardless of their personal background. Qualifications and the selection process Applicants for this position must hold a PhD degree (or equivalent level of education) in bioinformatics, data
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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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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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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
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; Collaborating closely with the ENGREENIT’s PhD candidate (starting 12 months later than the PostDoc researcher), supervised by the Assoc. Prof. Emil Dražević, and will jointly develop the heterogeneous
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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