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intelligence, computer science, remote sensing, geomatics, data science, or a forest/environmental science discipline with a strong quantitative or AI component Strong knowledge of machine learning and deep
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for more information. You must have a relevant Master's degree in civil engineering, structural engineering, mechanical engineering, railway engineering, transport engineering, computer science, data science
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learning, computing, data science, biomedical engineering, or a related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with
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stability assessment of converter-dominated transmission systems. The research combines power systems, numerical methods and scientific computing, with a strong emphasis on software development and practical
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classification • Collaborate within an interdisciplinary team of physicists, chemists, molecular life scientists. • Present your findings at conferences and in scientific publications. Your profile We are looking
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activities, such as assisting in courses of our computing science programmes. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD
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the EU Research Framework Programme? Not funded by a EU programme Reference Number 184188 Is the Job related to staff position within a Research Infrastructure? No Offer Description Advanced statistical
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | 2 days ago
, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. In the department
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generation of interdisciplinary experts who merge clinical endocrinology, AI, data science, engineering, ethics and law into an integrated field of digital endocrinology. The programme focuses on adrenal
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within the M2i framework Your work environment You will be part of Team Dey within the Computational Materials Science section at TU Delft. This team focuses on atomistic simulations to investigate