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device health status through condition monitoring. AI techniques such as machine learning will be used to optimise gate driver performance and to map gate drive signal attributes to power device health
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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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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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environment and the generation of microstructure-based crystal plasticity models of additive manufacturing. Develop machine learning (ML) models to accelerate virtual material testing and verification. Generate
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for AI research according to CSRankings . The PDRA will be supervised by Dr. Antonio Vergari, a leader in tractable probabilistic machine learning and neuro-symbolic AI, and will collaborate with
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, including potential advancement to Associate Professor. Teaching & Curriculum Development Design and deliver undergraduate and postgraduate courses in areas such as mathematical statistics, machine learning
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. This PhD project will build directly on this work by using ideas from machine learning—originally developed to study the movement of larger organisms—to understand how bacteria process information in
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machine learning-based analysis of two-phase flow behaviour. The successful applicant will be based at the University of Nottingham and spend time annually at UKAEA Fusion Technology Facility in Yorkshire
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survival is shaped by resource availability, competition, and cultural knowledge of food resources. You will work with an established bioacoustic dataset, a validated call machine-learning classifier, and a
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the third Higgs boson decays to two tau leptons, or another highly sensitive combination. The student will gain expertise in machine learning techniques for signal-background discrimination and will