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Organisation Job description We are seeking a candidate for a PhD project on the philosophical aspects of data science and data science use, with specific attention for the use of data science
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consortium comes in. PRELIFE unites experts across a wide range of disciplines from astronomy, biology, chemistry, computer science, earth and planetary sciences, education, mathematics, to physics. Together
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skilled in computational chemistry and data-driven approaches to develop new catalytic asymmetric reactions. World-leading industrial partners with a wide range of interests will provide advice on which
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for Energy Business and Economics Research (CEnBER - https://www.rug.nl/cenber/ ) and in the research programme Economics, Econometrics & Finance of FEB’s Research Institute. The project will be supervised by
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technologies and manufacture processes with emphasis on mechanical and materials engineering. For more information about the APE group please use the following link: www.rug.nl/research/ape. Qualifications
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The successful candidate should have: Master’s degree in Industrial Engineering and Management, Mechanical Engineering, Energy Systems, Control Engineering, Data Science, or a related field, with solid foundations
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. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data analysis. For more
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. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data analysis. For more
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the following traits: Educational background: A master’s degree in computer science/artificial intelligence with a solid grounding in optical and spectroscopic instrumentation, such as hyperspectral, NIR, and
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item