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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability
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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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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines. The University actively supports equality, diversity and
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Deadline: 30 September 2025 A fully funded 3.5 year PhD position is available to work on the project titled “Scalable benchmarking for digital quantum computers based on blind testing
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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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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
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@ch.cam.ac.uk , including 'PhD CI ¿ October 2026' in the subject. How to apply Please apply via the University Applicant Portal. For further information about the course and to access the Applicant Portal, click
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, Structural & Environmental Engineering, Trinity College Dublin, invites applications for a PhD Researcher to join the geotechnical research group as part of a Research Ireland funded research project
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, Structural & Environmental Engineering, Trinity College Dublin, invites applications for a PhD Researcher to join the geotechnical research group as part of a Sustainable Energy of Ireland funded research