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intelligence, NLP, machine learning, or a related field Experience with Python and Generative AI libraries (e.g., Huggingface Transformers) Knowledge of Multimodal Generative AI models and their corresponding
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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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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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Studentship partners: University of Salford & The Academic Department of Military Rehabilitation Supervisors: Academic: Prof Paul Comfort (lead), Dr Christopher Bramah, Dr Nicholas Ripley Industrial
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This project is a collaborative research effort between
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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), computation (bioinformatics, machine learning, statistical analysis), working with animals (radio-tracking, animal handling/sampling), and deep knowledge of evolutionary biology and gerontology. The Norwich
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econometrics, machine learning, and GIS for predictive housing price modelling Addressing Edinburgh and South East Scotland's Construction Skills shortfall The full description of the projects is available here
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural