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effectiveness and safety. This PhD project aims to address this challenge through biomimetic engineering design, combining predictive in silico modelling with machine-learning techniques and microfluidic
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BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. A long-term attachment (6-12 months) and
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. Stefanos Zafeiriou can be found at https://wp.doc.ic.ac.uk/szafeiri/ . Research Associate: A PhD (or be close to completion) in an area pertinent to the subject area, i.e. computer vision, machine learning
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and materials engineering. They will do this by integrating modern data-centric approaches, such as physics-informed machine learning, structure-aware modelling, and digital-twin methodologies, with
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organizational and collaborative skills, is essential. In this role, you will undertake a collaborative research project between the University of Oxford and Neumora (https://neumoratx.com), funded by the Michael
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complements existing departmental strengths in machine learning, multimodal data integration, digital phenotyping, and trial emulation using electronic health records. This is a full-time post (35 hours per
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other large scale machines (e.g. hydro-electric power stations, ships propeller bearing) sliding type or ‘hydrodynamic’ bearings [4] are much more common. There is increasing interest from industry
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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group) EPSRC Centre for Doctoral Training in Sustainable Sound Futures PhD
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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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success in the use of computer software application and statistical packages The following would be considered assets: Certification in Medical Education Certificate in university teaching and learning