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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 8 days ago
Status Full-time Hours Per Week 35 Offer Starting Date 23 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number Fellow_BI/FCT_Proj2025/i3S
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-erklæringen var sist oppdatert 23.02.2026 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde
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of Informatics and is hosted jointly by the Network and Distributed Systems Research Group and the Robotics and Intelligent Systems Research Group. The research groups consist of around 30 full- and part-time
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exposed to laser-accelerated intense proton beams. Primarily, the tasks are to be done by means of analytical calculations and high-performance computing simulations in a strong link with the industrial
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department upon appointment. If a 4th year is granted, one year of career-promoting work associated with teaching, dissemination or research infrastructure/services, will be distributed over the full
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23 Feb 2026 Job Information Organisation/Company University of Bergen Department Department of Informatics Research Field Computer science Mathematics Engineering » Electrical engineering Researcher
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Full-time Hours Per Week 35 Offer Starting Date 1 Apr 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 2023.18160.ICDT Is the Job related
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the display and the distribution of processed data. Related projects and responsibilities will include: Creation of artificial intelligence algorithms that effectively integrate molecular, pathology/image, and
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) Positions PhD Positions Application Deadline 18 Mar 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution