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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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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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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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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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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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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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experimentation and finite-element modelling. Research themes would be flexible including green steel formability under the EPSRC ADAP‑EAF programme for automotive and packaging applications; or micromechanical