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                The Natural Language Processing Section at the Department of Computer Science, Faculty of Science at the University of Copenhagen invites applicants for a PhD fellowship in Explainable Natural 
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                . Isabelle Augenstein, Department of Computer Science, email: augenstein@di.ku.dk , main project supervisor is Pepa Atanasova, email: pepa@di.ku.dk . The PhD programme You can undertake the PhD programme as 
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                one PhD fellowship in Philosophy of Science for a Doctoral Candidate (DC). The successful applicant will join a multidisciplinary and international MSCA Doctoral Network called TRANSCEND , which will 
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                Research Infrastructure? No Offer Description Department of Mathematical Sciences Faculty of Science University of Copenhagen Within the European Union’s Horizon Europe research and innovation programme 
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                of Copenhagen is offering one PhD fellowship in Philosophy of Science for a Doctoral Candidate (DC). The successful applicant will join a multidisciplinary and international MSCA Doctoral Network called 
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                PhD Project in Theoretical High Energy/Mathematical Physics Niels Bohr Institute Faculty of Science University of Copenhagen The Niels Bohr Institute invites applicants for a PhD fellowship in 
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                the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Department of Veterinary and Animal Sciences 
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                Rossmeisl, Department of Chemistry, Jan.Rossmeisl@chem.ku.dk , Direct Phone: +45 5071 9584 The PhD programme Qualifications needed for the regular programme (5+3) To be eligible for the regular PhD programme 
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                30 Sep 2025 Job Information Organisation/Company University of Copenhagen Department Department of Food Science & Niels Bohr Institute Research Field Biological sciences Chemistry Physics Researcher 
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                include data science management and development of novel and executing existing computational methods including machine learning and deep learning methods to integrate genomics, transcriptomics and