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Field
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develop foundational security theory for agentic and multi-agent AI systems, as part of our research programme in multi-agent security. You will build formal models of security-relevant behaviour in
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independently in techniques such as Mass spectrometry, and traditional laboratory-based techniques such as histology (IHC and IF), western blots and ELISAs. competency working with small animal models, and
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of persistent virus. 3. Single cell analysis of in vivo viral reservoirs and in vitro models of latency. 4. The innate immune response to HIV and mechanisms of immune evasion. The successful candidate will join a
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databases and genome-scale metabolic models. Additionally, this fellow will develop robust error-checking and validation mechanisms to minimise misinterpretations. Applicable legislation and regulations
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. Proficiency in using PyTorch and other machine learning libraries for developing and implementing machine learning models is essential. Ability to prepare comprehensive written reports, including the use
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, group theory, and/or graph theory will be necessary. Experience in modelling biological processes, and in algorithm development or computation will also be valuable. Proven commitment to proactively
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering large AI models or their applications. Current PLI projects involve work with
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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enforcing quantitative validation against helioseismic constraints. The project combines surface-cooling-driven convection, compressible fluid dynamics, and magnetic fields in ultra-high-resolution models