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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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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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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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sustainable heritage management decisions (particularly in an African context), using advanced methods in satellite imagery analysis, remote sensing and machine learning, combined with geospatial analysis
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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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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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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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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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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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural