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funded through the EU Research Framework Programme? Other EU programme Reference Number NOTICE E2S/P-20/2026 Is the Job related to staff position within a Research Infrastructure? No Offer Description
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them with every possibility for networking. Training: Personalised and Responsive Close personal supervision by three professors Individual and flexible course programme Methodological and theoretical
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within three months of your employment contract start date, and that you participate in an organized doctoral programme through out the period of employment. As an employee at NTNU, it is important that
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inspiring people across disciplines and with a variety of perspectives and backgrounds. The Faculty has six departments: Biology, Pharmaceutical Sciences, Information & Computing Sciences, Physics
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the Attoscience and Ultrafast Optics group led by Prof. Dr. Jens Biegert. Our PhD-program brings together top-level training and teaching for young scientists , benefiting from the extensive course offerings of
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philosophy and/or social studies of science and technology. The position is part of the Horizon MSCA Doctoral Network “Towards sustainable computational fluid dynamics” coordinated by the Institut National
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» Computer engineering Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 25 Sep 2026 - 23:59 (Europe/Athens) Country Cyprus Type of Contract To be
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19 Aug 2026 Job Information Organisation/Company University of Innsbruck Department Faculty of Law Research Field Juridical sciences Researcher Profile First Stage Researcher (R1) Application
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
15 Jul 2026 Job Information Organisation/Company Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial Department Human Resources Research Field Engineering » Computer
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with a strong background in mathematics, computer science, or machine learning. The work has a strong focus on developing new objectives or new architectures for medical deep learning and on new ways