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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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will be required due to regulated activity involving children and ‘at risk’ adults. The successful candidate will hold, or have submitted a relevant PhD/DPhil (or equivalent) in a relevant discipline
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data from patient samples, including DNA methylation, histone modifications, single-cell transcriptomics and chromatin accessibility. You will contribute to the study and sequencing design, collaborate
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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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synaptic function and synaptic loss, and keep meticulous, detailed records of your work and commit to engaging with cloud-based analyses on the IMCM data platform. Other duties will include collaborating
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changes. In this role, you will take a lead on developing data analysis approaches and contribute to the overall study design and implementation. In particular, this will involve applying existing or even
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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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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
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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