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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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machine learning with physics-based failure modelling to enable real-time fault detection and predictive maintenance strategies. Validation of AI models with real-world SCADA data, ensuring industry
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markers. Develop machine learning models capable of predicting Category 1 emergencies based on real-time audio features extracted from calls. Work iteratively with YAS researchers to test and refine
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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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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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection