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on correlation-based machine learning. When an agricultural system fails due to compounding climate extremes - like a simultaneous heatwave, drought, and ozone pollution spike - standard models can forecast the
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this specific structured data. How can we perform inference tasks to learn hidden patterns, like community structure or hidden hierarchies? How can we incorporate domain knowledge to design interpretable models
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machine-learning algorithms, and with lightning-fast Maxwell solvers for scattering simulations. You will not only work on the 3-D models in theory; you will also be trained in operating advanced microscopy
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. You will combine technical work on machine learning with qualitative analysis of how AI systems are interpreted and used in organisational decision-making. Join the Human-Centred Computing group
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learning algorithms. Personalizing user interactions by building models that adapt explanations to specific knowledge levels and interests of users, so that user modelling and formal reasoning transform
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knowledge of and/or experience with validation of prediction models (regression or supervised machine learning), health technology assessment, decision curve analysis, and/or value-of-information analysis
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degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch
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modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning). Experience in annotation software such as ELAN and PRAAT. Existing peer-reviewed journal publications and conference
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that requires tightly integrated approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models, with emphasis
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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a