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and its stakeholders. You should hold a Master’s or PhD (desirable) in Computer Science, Artificial Intelligence, Machine Learning or a closely related field. We are looking for a candidate who has
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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culture or legal practice-oriented and legal skills teaching. These posts are open both to those with a research background and to those with legal practice or teaching and learning experience who will be
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to continuous improvement. Contribute to the development of appropriate programmes, modules and lectures in accordance with academic and military learning objectives. Participate fully in assessment
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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focus may be considered, but preference will be given to candidates with subject matter expertise in the following areas: disruptive technology and AI; Russian strategy and doctrine; military operations
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chemical reactions and assembling functional nanomaterials from bottom-up in scanning tunnelling microscopy (STM). The project is tightly linked to machine learning algorithms in images (image classifier
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disciplines, exemplified by its internationally recognised research in AI, computer vision and machine learning, as well as in networks and cyber security. The School hosts an Academic Centre of Excellence in
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is funded by EPSRC, titled “Adopting Green Solvents through Predicting Reaction Outcomes with AI/Machine Learning”, involving academic investigators from 3 institutions (Imperial College London