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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes
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qualities include: earlier research experience, e.g., as part of Masters’ studies, and familiarity with machine learning, formal methods or network protocols are considered as merits. Your workplace
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look forward to receiving your application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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Helps us to derive novel climate data by combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position
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. The candidate is expected to have an overall interest in AI concepts and methods, in particular human-centred AI, and expertise in formal models and machine learning, as demonstrated by publications and other
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slowdown at the glass transition, remains a major computational challenge. This Doctoral student project addresses this by combining generative AI models and machine-learned interatomic potentials
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slowdown at the glass transition, remains a major computational challenge. This Doctoral student project addresses this by combining generative AI models and machine-learned interatomic potentials
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and