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, potentially including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during
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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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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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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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. The successful candidate will develop advanced skills in multi-modal sensor fusion, signal processing, machine learning, and integrity assessment, as well as transferable abilities in critical thinking, project
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validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue
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motivated PhD student to join our interdisciplinary team to help address critical challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make
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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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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human