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the quality of uncertainty estimates by standard methods for LLMs, particularly with deep generative models, and (iii) develop a benchmark for uncertainty quantification in LLM-based scientific agents. The main
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, system-wide efficient, as well as fair for heterogeneous participants. Addressing these challenges requires new mathematical models and algorithms that blend optimization, game theory, and control with
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problems and generative models. This PhD project investigates how generative AI models, particularly diffusion models, can be used as prior distributions in Bayesian inverse problems. The aim is to develop
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rock. As a PhD student, you will work closely with experts in rock mechanics, grouting technology, groundwater modelling, and reliability-based design. Your tasks will include: Investigating how
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high-temperature systems. Proven ability in process modeling and simulation, preferably using Aspen Plus. Ability to conduct independent research, plan and analyze experiments, and document results
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cities in implementing effective climate action strategies through spatial planning. The appointee will be responsible for leading the modelling work on this project and coordinating the research and
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tract. The research will build spatiotemporal models of human immunological tissue architectures, revealing how immune niches form, evolve, and malfunction across human organs and diseases. This includes
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development, reactor design, and process operation, supported by process modeling in Aspen Plus. The research will explore both fundamental reaction mechanisms and practical system performance, combining
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validated predictive models of pavement deterioration. The position is research-focused, with opportunities to strengthen independence and build qualifications for future academic or industry careers
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of large-scale machine learning models (e.g., LLMs) in a meaningful way, we, therefore, need new scalable methodologies that can efficiently and accurately capture, represent, and reason about uncertainties