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water is crucial for rational design of formulation in the pharmaceutical industry. The aim of this project is to implement an automated robotic platform for programmable high-throughput combinatorial
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into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic
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Join us in the University of Exeter’s Centre for Metamaterials Research & Innovation – the heart of the UK Metamaterials community. You’ll be part of the £20 million EPSRC MetaHUB – the UK’s largest
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representations with probabilistic heads (GPs, Bayesian neural networks) for calibrated uncertainty estimates. Finally, the PhD candidate will focus on \textbf{active learning / adaptive design} for MF settings
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. The goal of this project is to advance gene regulatory network (GRN) inference from multi-omics data by developing novel AI techniques that exploit the knowledge of gene perturbations (experimental design
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organisations, and schools to co-design practical strategies and supports that enhance physical literacy–aligned practice. These may include coaching prompts, planning tools, professional learning modules, and
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rely heavily on informal learning and personal experience to guide culturally responsive practice, with limited access to structured, evidence-informed support through formal coach education
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. The core innovation is embedding expert judgement, regulatory constraints, and equity objectives directly into policy learning using structured preference feedback and explicit constraints. The framework has
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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The Max Planck ETH Center for Learning Systems (CLS) addresses cross-disciplinary research questions in the design and analysis of natural and man-made learning systems. The excellent engineering