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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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PhD at the Forefront of Computational Solid Mechanics and Machine Learning School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application
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at the intersection of machine learning, bioinformatics, and computational pathology. Project Overview: Integrating histopathological imaging with omics (e.g., transcriptomics, genomics, proteomics) holds tremendous
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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to investigate the conditions that support forest recovery after a fire. The study areas can be defined using...
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters