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We are looking for a talented and enthusiastic candidate for a fully funded 4-year PhD position. The PhD candidate for this project will be working at the RNA Structural Ensemble Dynamics group led
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you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase
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. Process simulation software is being developed for virtual optimization of tool design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies
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to monitor patients’ health condition continuously and accurately after surgery to measure and evaluate patients’ recovery progress, timely detect and even predict clinical adverse events like delirium
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. The principal goal of the PhD project is to develop component models with a greater physical accuracy and predictive capability by employing state-of-the-art methods for the following two modelling approaches
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handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during forming. With
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predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc.). Therefore, various data sources including real-time traffic counts from inductive loop detectors
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-throughput experiments to train AI tools to predict properties of complex mixtures? Then join our team as a PhD candidate! Chemistry is a science of mixtures. Whether you think of complex formulations for drug
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design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc