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an advantage. The appointee will work with Prof. Wai Sze Chan on projects related to developing predictive models for health outcomes and digital behavioural interventions. Responsibilities include managing and
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to better understand and characterize variability of water at the land surface - i.e. in soils, snow and groundwater - to help in predictions of future water availability, global water cycle dynamics and sea
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other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases
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of methods for real-time prediction of quasi-periodic hormone profiles and the detection of early signals for disease by combining mechanistic modelling with methods from machine learning to enhance inverse
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new genomic tools and predictive models to understand ecosystem resilience, climate change impacts, and environmental health. In addition to advancing fundamental science, the initiative seeks
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. The programme will explore the biodiversity and biological adaptations of plants, animals and microorganisms to extreme mountain environments, while developing new genomic tools and predictive models
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. This role is ideally suited to a researcher with experience in computational structural biology, glycobiology, or protein modelling, who is interested in applying AI-enabled structural prediction and glycan
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control theory (e.g., model-predictive control, etc.), data-driven modelling and control methods (e.g., reinforcement learning, transfer learning, etc.) Proficiency in programming tools and languages, e.g
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traction motor noise and vibration in residential buildings, including conducting sound intensity and vibration measurements, analysing acousto-structural transmission paths, developing predictive models
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models and simulations that deepen the understanding of the underlying physics involved. The ultimate goal is to create predictive, physics-based models that optimise and control pharmaceutical