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Computational modelling of the emergence of somatosensory cortical maps School of Psychology PhD Research Project Self Funded Dr Hannes Saal, Dr SP Wilson Application Deadline: Applications
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confidentiality. Why does this matter now? Wearable devices have made round-the-clock health data commonplace, cloud computing places vast processing power at our fingertips, and patients are demanding care
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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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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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components and control algorithms for the printing process that take into account our theoretical understanding of the processes occurring and utilizing high-speed in situ data acquisition to respond
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. To fill in this gap, in collaboration with industrial partners, the research will develop novel Machine Learning and Computer Vision methods for detecting and localising. These will be used to develop
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Overview This post is funded by the Quantum Computing Hub (QCI3) supported by the Engineering and Physical Sciences Research Council (EPSRC). The QCi3 Hub is part of Phase III of the UK’s Quantum
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machine vision algorithms. The system will be designed with the physical constraints of remote fusion environments in mind, including radiation tolerance, restricted access, and the need for automation and
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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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Phase Field Computational Methods for Fracture Problems in Engineering School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application Deadline