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health, and bioinformatics. You will apply advanced AI methods - from classical machine learning to large language models and agent-based AI - on large-scale healthcare datasets, including structured
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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, this interdisciplinary project will couple mathematical models of earthworm movement, stochastic models of the measurement process and designed experiments to improve earthworm detection. Project This project will work
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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural
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. Your work will feed directly into the development of predictive models that link microstructure to performance, guiding the design of alloys that are stronger, more reliable, and more efficient. By doing
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a
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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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signs of cardiovascular changes, adaptively model physiological patterns, and identify predictive biomarkers of maternal health. You will develop and apply cutting-edge techniques in: Signal processing
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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Fixed-term: The funds for this post are available for 12 months A position is available for a Postdoctoral Research Associate (Postdoc) in the forthcoming Aspirational Computing Lab in