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Computational Astrochemistry/Algorithm development for Quantum Dynamics Calculations School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof AJHM Meijer Application
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for application to fusion reactors, considering factors such as muon flux, detector sensitivity, and data analysis algorithms. PhD candidates will be tasked with developing simulations of complete detectors and
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guaranteed? This project will focus on developing theory and algorithms for MPC applied to the smart grid. The emphasis is on developing implementable (low-complexity) controllers with strong theoretical
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of data from in-Situ AM Process Monitoring tools, machine agnostic algorithms will be generated for quality control. Knowledge transfer of the methods developed onto industrial machine platforms will be a
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experimental research as well as data analysis and algorithm development. Students with either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in engineering, mathematics, neuroscience
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transmission methods (wired or wireless) will be optimised for robust data capture in natural sleep environments. AI-Driven Analysis: Develop advanced AI algorithms to analyse the collected sensor data, aiming
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year round Details Research at Sheffield has been developing models of the railway network from a range of perspectives including smart-grid energy consumption, passenger satisfaction, and integration
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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participate in developing algorithms for tau lepton identification, and will also have the opportunity to assist with silicon module construction for the ATLAS tracker upgrade. Instructions for applying can be
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Directly Funded UK Students Dr Jon Willmott, Dr Matthew Hobbs Application Deadline: 23 June 2025 Details The fusion energy sector must develop methods of remote maintenance where human access is impossible