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to the 4th February 2026. You will be investigating the safety and security implications of large language model (LLM) agents, particularly those capable of interacting with operating systems and external APIs
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, TensorFlow), with a particular interest in interpretable models, dynamical systems, and the development of LLM-based agents for clinical reasoning. Experience working with healthcare or genomic datasets is
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. • Familiarity with agent-based and compartmental models for infectious diseases. The grade of appointment will be accorded based on candidate’s academic qualifications and years of relevant experience. Applicants
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in ecological fieldwork, ideally in wet grassland or agricultural systems - Skills in GIS, remote sensing, and spatial data analysis (bonus: agent-based modelling) - Demonstrated ability to work in
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sparse-regression based techniques to derive interpretable and computationally efficient differential equation models from computationally intensive multi-cellular agent based models (ABMs) of Epstein–Barr
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of agent-based and/or continuum modelling, analysis and simulation. You will have experience of programming in Python and C++, or demonstrated ability to rapidly acquire fluent knowledge of new programming
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function independently to develop therapeutic agents against mutant p53 and other oncoproteins using artificial intelligence, monoclonal antibodies, and DNA vaccines. The positions offer a unique team-based
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plan: The work consists of developing models for the prediction of biological control agents (BCAs), using different approaches: Machine Learning (random forests, support vector machines, lasso), Deep
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Process: designing, preparing, shaping and characterizing materials in order to discover, control and optimize specific functions. The ICMCB carries out fundamental research on model materials and/or
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the development of components using agent-based modeling techniques, reinforcement learning, and other explainable artificial intelligence modules for decision-making, situation assessment, and operational support