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experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools; Code synthesis
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recruitment and clinical assessment activities and helping design the neurofeedback protocol, including monitoring data quality for clinical interviews, data collection of online databases of self-report
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; testing hypotheses and analyzing data from a variety of sources, reviewing and refining working hypotheses as appropriate; engage with industrial stakeholders to understand needs and disseminate results
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to implantation, including validating methods in virtual in vivo environments. • Collaborate with engineers to refine fuel cell specifications and support in vivo experiments. You should hold a relevant PhD
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of the project. Applicants should hold, or be close to completion of, PhD/DPhil in Biology or a related subject. You should have a high level of competence in cell biology and relevant experience demonstrated by
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for differential rate correction). The successful applicant will hold, or be close to completion of, a relevant PhD/DPhil in bioinformatics, comparative genomics and phylogenetics, together with relevant experience
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have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track
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evaluations, attacks on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related
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responsible for managing your own academic research and administrative activities and adapting existing and developing new research methodologies and materials. You will analyse quantitative data from a variety
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and structural biology of rhomboid-like membrane proteins. You should hold a PhD/DPhil in a topic relevant to structural biology and biochemistry, together with relevant experience. You should be able