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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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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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experience includes: Nano-imaging or sensing methods Optical or vibration detection technologies AI/machine learning for imaging and sensing Background in biology, microbiology, or biomedical sciences
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PhD: Systematic Exploration of Robot Behaviours for Manufacturing Tasks to Automatically Discover Failure Scenarios EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness
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to learn laboratory methods for analysis of relevant BGC parameters. Training: You will be based in the Polar Oceans Team at British Antarctic Survey, a highly active research team focused on both
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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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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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. 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