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, ecology, and conservation and spans a range of activities from exploratory analysis, visualization, and discovery to prediction, validation, quantification of uncertainty, and inference. To thrive in
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, Secure and Federated Learning or Statistical Learning and uncertainty quantification. Experience of course development and the use of a range of both innovative and traditional pedagogical practices is
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model development, emulation, uncertainty quantification, statistical modelling incl. ML and hybrid modelling) in collaboration with other Hutton specialist departments and Biomathematics and Statistics
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certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of machine learning, uncertainty quantification, and Bayesian modelling. They will provide complementary expertise to bridge agentic AI with real-world impact. What We Are Looking from You Background in
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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
technical avenues like self-supervised learning, physics-informed deep learning, uncertainty quantification, interpretability, and explainability in deep neural networks, attention-based approaches
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) Task 5 – Biosensing technology - Electrochemical platforms This project aims for the PhD student to apply advanced biosensor technologies for the detection and quantification of allergens in bioaerosols
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interest include physics-based surrogate modeling, uncertainty quantification, and multi-modal sensing, especially as applied to civil engineering systems. The successful candidate will complement and expand
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interest include physics-based surrogate modeling, uncertainty quantification, and multi-modal sensing, especially as applied to civil engineering systems. The successful candidate will complement and expand
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tracking error. The aim is decision-grade uncertainty quantification (UQ) and principled data-driven parameter selection. Hence, the project will develop automatic portfolio rebalancing driven by UQ analysis