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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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of the mechanisms that lead to defect formation in DED-LB, and improve process control measures to either prevent or mitigate the defects. The additionality of external controls over the laser-matter interactions
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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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Position Summary: Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Control and Power Research Group, Department of Electrical and Electronic Engineering
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methods for defect detection; Apply AI and machine learning techniques to process, analyze, and interpret complex NDE data; Create AI models for automated defect detection, classification, and
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Grid Solutions Ltd on behalf of GE Vernova. The project’s topic will revolve around advanced high-voltage power electronics design and control, addressing both academic and industry needs. HVDC
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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into advanced turbofan configurations. This doctorate will research highly innovative technologies that carefully control the temperature of key engine components. The Oxford Thermofluids Institute
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Research Studentship in Numerical Optimisation and Control 3.5-year D.Phil. studentship Project: Embedded Optimisation for Autonomous Spacecraft Control Supervisors: Prof Paul Goulart The project
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seeks to enhance the predictivity, accuracy and applicability of FEA for WA-DED, enabling more efficient design and control of large-scale additive manufacturing processes. The student will be based