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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
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, too computationally demanding for real-time use, and lack reliable uncertainty quantification. The methods developed in the project will tackle these shortcomings, enabling computationally efficient
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lack reliable uncertainty quantification. The methods developed in the project will tackle these shortcomings, enabling computationally efficient inference and prediction of gas dynamics at high spatial
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, MATLAB, R, or Julia). Curate and integrate experimental data to calibrate and validate models, including parameter estimation and uncertainty quantification. Work collaboratively with experimentalists and