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parameters using experimental muscle and neural recordings Explore motor control policies that replicate observed behaviours Test simulation predictions against muscle ablation experiments Investigate how
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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processes, targeting annual savings of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real
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into model-predictive control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture
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mechanisms of adaptive and acquired drug resistance, exploring network-level control and feedback in cell signaling systems, identifying novel drug targets and therapeutic strategies, and developing predictive
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of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real-time data acquisition. You will
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Modeling and outputs. Implements Strategic Modeling governance, data-quality controls, and version-management processes. Leadership, Team Management, and Staff Development Directly supervise two Strategic
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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of pharmaceutical formulation and manufacturing processes. The role The post holder will develop and implement mechanistic models to analyse and predict the behaviour of pharmaceutical processes. Your work will