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a core engine of adaptive development. Infants are conceptualised as active agents who shape their own learning by sampling information, reducing uncertainty, and updating predictions. We are building
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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deployment, agent orchestration, or data source ingestion. Work Arrangement: Please note that the described work arrangement is subject to change based on business needs and is not guaranteed to be final
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-tuning, evaluation, benchmarking, and optimization of large language models (LLMs) and other AI technologies to support research and operational objectives. 6. Design, build, and deploy AI-powered agents
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theory and/or agent-based models) to formalize strategic interactions involving algorithmic pricing technologies. • Empirical analysis of real-world pricing data (e.g. e-commerce platforms) to detect
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). This interdisciplinary project investigates how AI — specifically large language model (LLM)-based agents — can act as adaptive social agents to support students' collaborative learning in Challenge-Based Learning (CBL
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, preferably related to serious games and/or agent-based modeling and simulation Desired experience: 2 to 5 years of professional experience Where to apply Website https://www.aplitrak.com/?adid
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multiscale, off-lattice agent-based model of tumour initiation and clonal evolution using Chaste, an open-source simulation framework for cell populations co-developed in Sheffield (Cooper et al., 2020, doi
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explore new mathematical and computational approaches to represent such systems, drawing on ideas from stochastic processes, four-dimensional (space-time) modelling, complex systems, agent-based modelling
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to science, engineering and society. You will focus on optimization-based, data-driven and partially model-based control methods. You will have access to a strong research network and a broad range of