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Field
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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Boreal forest recovery after clear-cut: We are looking for a highly motivated PhD student to collect data on and model hydro(geo)logy and greenhouse gas exchanges of boreal forests after
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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of contact (e.g. online chat, e-mail or telephone) and explore potential linkage to other helpline datasets (e.g. AgeUK). In the 2nd and 3rd year of the PhD research, the data will then be used to conduct
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This collaborative project between Jaguar Land Rover (JLR
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a team to undertake a PhD in the Optics and Photonics Research Group (OPG), supervised by Dr. Mitchell Kenney alongside collaborators within OPG and Life sciences. (https://www.nottingham.ac.uk
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programme’ page, please select (Neuroimaging) Research MPhil/PhD (Full-time). More information on the department and the programme is available at the departmental prospectus page here: https://www.kcl.ac.uk