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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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moisture risks, directly affecting children’s health, wellbeing, and learning outcomes. This PhD will develop evidence-based, Passive House–informed retrofit strategies tailored to diverse school typologies
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into the sample of interest. Recently we have been using AI and machine learning to predict the distortion present and significantly speed up this correction process. This PhD project will take the latest in AI
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using multimodal approaches including advanced imaging, nano-mechanical characterisation and machine learning techniques Developing physics-informed reliability models using experimental datasets
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A Human-Factors Investigation of Automation, Decision-Support and Machine Learning in Clinical Decision-Making Tasks. This PhD project is based within the Human Factors Research Group in the Faculty
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years
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related to medicines synthesis. Eligibility and Application: We are seeking an enthusiastic and motivated candidate with a strong willingness to learn new disciplines and innovate to achieve project goals
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). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
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equivalent)) Knowledge of simulation platforms like MATLAB Simulink/PLECS. Coding and hardware skills are desirable. Strong analytical/mathematical skills. Passion for research and willingness to learn. Good
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and take initiative, good hands-on problem-solving skills, and the capacity to acquire new skills. Ability to actively contribute to a collaborative and supportive research team, and willingness