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are produced, the molecular mechanisms by which they have achieved their improvements will be investigated. These insights will form a key part of the ‘build, test, learn’ cycle to accelerate the development
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Are you fascinated by the chemistry of the gut microbiome? Are you intrigued to learn more about how the bacterial metabolic function is involved and could be targeted in the context of immune
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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. Change. Impact! Faculty Mechanical Engineering From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its
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-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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global scale. That is why we invite you to apply. Your application will receive fair consideration. Challenge. Change. Impact! Faculty Mechanical Engineering From chip to ship. From machine to human being
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with exceptional precision and in unexplored energy regimes. We use a crossed molecular beam machine with a Zeeman decelerator, which enables precise control over the velocities and quantum states of
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, creativity, rigor, ownership, and excitement to push research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, data management, and