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truthtfulness. Moreover, the training and theory of LLMs has no notion of epistemic uncertainty. Recently AI researchers pushing world models address this problem and one alteBeinrnative is training objectives
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these issues is critical for building trustworthy multimodal AI systems. Research Objectives The goal of this PhD project is to develop scalable Bayesian uncertainty estimation frameworks for single- and multi
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training objectives are intrinsically risky and have proposed an alternative paradigm: a non-agentic "Scientist AI" that explains the world from observations rather than acting in it, combining a world model
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for mathematics across the CWTS Leiden, ARWU, USNews, and QS rankings. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data Science
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for mathematics across the CWTS Leiden, ARWU, USNews, and QS rankings. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data Science
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, Combinatorics, Geometry, Mathematical Physics and Number Theory, Bayesian and Monte Carlo Methods, Mathematical Modelling and Biomathematics, Quantum Mathematics, Biostatistics and Ecology, Computational
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to maintain high label efficiency in non-stationary environments, supported by reproducible benchmarks and principled evaluation protocols. Key Objectives O1: Drift-aware querying: Develop acquisition functions
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interests in the areas of applied and pure mathematics, and statistics. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data