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to the 31st January 2027 and is funded by OpenPhilantropy. We are seeking candidates for a research project focused on benchmarking Agentic LLMs. Specifically, we are looking for a PDRA to lead our project
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assurance, financial crime, synthetic data, LLM orchestration, and agentic workflows? We are seeking a Postdoctoral Research Associate (PDRA) in Artificial Intelligence and Reinforcement Learning to join a
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structural maintenance of chromosomes (SMC) complexes shape chromosomes by DNA loop extrusion, how they cooperate with DNA topoisomerases, and how bacteriophages and other infectious agents manipulate host
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. There will be three research fellows appointed in different stages of the project. For this post, the successful candidate will primarily focus on developing LLM agents to support effective theory of mind
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(e.g., Reinforcement Learning, Agent Based Modelling) to join our team full-time as part of a large international collaboration of European researchers (incl. Tobias Dienlin, Veronica Kalmus, Adrian
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social media analysis, smartphone-based sensing, and agent-based modeling. Combining macro-level patterns with micro-level behavioral data, it will examine how polarized content influences stress, anxiety
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new antibiotics and explore new potential antimicrobial agents. We have a world-class track record of success and a reputation for a can-do attitude to tackling challenging goals. At the heart of our
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or orthotopic tumour models Supporting preclinical treatment studies involving standard-of-care or experimental agents Applying in vivo imaging techniques (e.g., bioluminescence imaging) to monitor tumour
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the problem of induction”. How biological and artificial agents can use limited evidence to effectively learn and generalise is a long standing issue for psychology, AI/computational sciences, neuroscience and
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the capabilities and efficiencies of physical agents, ranging from service robots, self-driving cars, and heavy machines. What We Offer: A rich research environment equipped with cutting-edge robot platforms