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cognitive science. We are looking for an applicant who can strengthen our research profile in computational modelling of cognitive processes and/or computational modelling of social processes. The ideal
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learning, big-data, computer vision, or related fields, as well as experience in in-line process monitoring or similar areas. Preference will be given to candidates with competence in concrete
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Foundation Quantum Computing Programme (NQCP) is establishing a talented and diverse international team to create a cutting-edge quantum programme in the heart of Copenhagen, Denmark. At the Niels Bohr
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Postdoc Position in Models of Quantum Programming Languages (Sapere Aude: DFF-Research Leader Pro...
The Department of Mathematics and Computer Science at the University of Southern Denmark, Odense, invites applications for a postdoctoral research fellowship in models of quantum programming
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Are you interested in neuromorphic spintronic and can you contribute to the development of the project? Then the Department of Electrical and Computer Engineering invites you to apply for a one year
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neuroscience, objective measures of auditory function, computational models of hearing, hearing-instrument signal processing, and multi-sensory perception. Our goal is to advance the understanding of the human
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The Centre for Machine Learning located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark invites
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letters. Closing date Feburary 28th 2026 Successful candidates will be asked to send an application to the PhD Secretariat, Faculty of Health Sciences, to be enrolled as PhD students. The PhD programme will
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an academic degree or other relevant education within the field of computer science, natural science or another related field We are looking for a candidate that has documented experience working with
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will