171 phd-communication-and-signal-processing Postdoctoral positions at University of Oxford
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agentic architectures for autonomous scientific reasoning and planning; • AI social scientists, including language-model-based and agent-based simulations for social science domains such as history
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project focusing on understanding signalling via inhibitory receptors in T cells using functional genomic screening approaches in primary T cells. The post is funded via the Wellcome Trust and is available
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using computer programs to design experimental paradigms, analyse data and conduct advanced statistical analysis. You will have excellent communication skills, including the ability to write
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; communicate effectively both orally and in writing; be self-motivated and able to motivate and guide the work of others. Previous experience in the commissioning and operation of storage rings and knowledge
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transcranial magnetic stimulation) to test the causal role of targeted circuits in cognitive processes. You will design and conduct research into human behaviour (habits, learning under uncertainty) that is of
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contribution of co-signalling receptors to this process. The post-holder will be involved in both molecular and cellular work. At a molecular level, the work will involve the expression, purification, and
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. With a background in testing of materials and numerical analysis, you will be self-motivated, able to plan and deliver a research project. You will have a PhD (or be near completion) in related topics
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months), and is full-time. Applicants will have, or be close to completing a PhD/DPhil in statistics, computer science, or related areas. They will have excellent communication skills, including
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proof-of-principle repetition-rate and staging experimentation. The successful candidate will perform duties that include developing/using particle-in-cell computer codes hosted on local and national high
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Planetary Physics (AOPP) sub-department of the department of Physics, at the University of Oxford. The post will address a significant problem in climate forecasting known as the Signal-to-Noise Paradox (SNP