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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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Electrical Capacitance Tomography (ECT) to study the combustion processes of hybrid propellant propulsion systems. To perform inflight tests using the ECT as hardware in the loop to control O/F ratios in a
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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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solutions for maritime propulsion systems. The primary aim of the project is to design and implement a flexible, high-performance control strategy for an integrated power system consisting of an ammonia
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present with neurodevelopmental deficits associated with Autism Spectrum Disorder and hyperphagia, and in healthy controls. We will be using a range of methods, including behavioural phenotyping, cognitive
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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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successes and proposes intelligent sensing and control solutions for automated robotic systems capable to be tele-operated using smart human-machine interfaces. This is an exciting PhD project that has a
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automated feeders), and analyse microbiome composition using 16S rRNA and whole-genome sequencing. Statistical modelling will test for links between microbes and host development and fitness.� PROJECT
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap