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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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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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of medicines, to ensure their safe and equitable use across society. In addition to UK based data, we will seek to collaborate with international partners to use world-wide data. Techniques will include
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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opportunities to develop scientific independence, establish new research directions, collaborate across disciplines, mentor junior scientists, and acquire the skills and publication record necessary for the next
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September 1, 2026, subject to final budget approval and is available for up to three years. The post-doctoral associate will spend half their time on independent research, and half their time on collaborative
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well as historians of physics who are interested in engaging seriously with the content of the science they study. You will be an active member of RCNP. You will collaborate with the other postdoctoral researchers
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. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from the SPRUCE (Spruce and
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knowledge Integrity: earning the trust of those we serve and each other through honesty, transparency, accountability, and continuous reflection Collaboration: approaching all opportunities with
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workshops and reflection sessions, contributing to collaborative and transdiciplinary knowledge development under the principle of “learning together, developing together"; Publish articles in both peer