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can feed directly into precision surgery algorithms and clinical trials. Few PhD projects offer such a clear line of sight from variant to mechanism to clinical translation. Located on the thriving
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of
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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human
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needs. By bridging human-centric innovation, generative algorithms, and sustainability metrics, this project seeks to redefine how novel products and systems are conceived, developed, and evaluated. You
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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experimental methods to improve them, alongside developing new approaches to enhance these properties. External environmental factors, such as humidity, pressure, and temperature, will also be key variables
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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" (Supervisor: Prof Timothy O'Leary) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust