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wireless system. Analyse experimental measurements using MATLAB, Python or equivalent computational tools. Compare experimental measurements with modelling or theoretical predictions generated by the wider
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. Relevant AI-enabling research areas may include, but are not limited to, reaction discovery and optimization, synthesis planning, catalyst and reagent design, mechanistic analysis, and prediction or control
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and AI-based approaches, including predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The scholar will have opportunities to lead first-author publications
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Experimentation The department investigates the dynamics of the ocean carbon cycle, its variability and predictability, and its role in the Earth system through climate-carbon cycle feedbacks operating across
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chain, ranging from synthesis, cell assembly, characterization, modeling to scaled-up manufacturing. The 2-year postdoctoral project Machine Learning-based Electro-Chemo-Mechanical Estimation and Control
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work closely with an interdisciplinary team spanning microbiology, engineering, and computation, and will contribute to developing predictive models that link bacterial physiology to infection outcome
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to deliver accounting and financial reporting, budgeting and forecasting, treasury and debt, internal controls and compliance, purchasing and procurement, accounts payable, payroll, and student accounts
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to develop predictive models for polymer-based materials. This project aims to leverage computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials
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Context: Controlling heat and particle fluxes on tokamak walls is a critical challenge for future tokamaks. Achieving burning plasmas near ignition while ensuring sufficient power distribution on divertor
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knowledge of their development and structure. Working closely with colleagues at the Universities of Leeds and Reading, you will integrate theoretical understanding, observational data, and modelling