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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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follow the EngD in Model-Based Systems Engineering Programme. They will be based at a Leonardo site in the UK. Entry requirements: A minimum of an upper-class honours degree (2:1) or overseas equivalent in
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begore the deadline. The start date is 1st October 2025. This studentship is related to a multi-institutional EPSRC Programme Grant "AMFaces: Advanced Additive Manufacturing of User-Focused Facial
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning
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practices. Within this framework you will: extend and use a process-based modeling approach which explicitly represents microorganisms and biomolecule functioning in soil systems. use process-based modeling
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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Directed Energy Deposition (DED) process for metallic components. The PhD candidate will focus on edge computing and the application of AI for data analysis and for identifying correlations with ground truth
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases