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About the Role The project “An Erlangen Programme for AI” (funded by the UKRI), will broadly involve applying advanced mathematical techniques for understanding training in neural networks, with
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imaging. This includes exploring efficient network designs, contributing to the development of novel learning-based representations for geometric reconstruction, and integrating insights from neural
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong communication and scientific
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computational modelling using artificial neural networks. It brings together teams led by Mohamady El-Gaby (Oxford Experimental Psychology), Matthew Nour (Oxford Psychiatry), Rick Adams (UCL), and Maria Eckstein
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modelling of the C. elegans neural network. The lab also uses Two Electrode Voltage Clamp (TEVC) electrophysiology and molecular biology techniques to characterise receptors. There are a broad range of
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being used by the clinical group in parallel with neurosurgical patients in Iowa. Our goal is to advance medical science by providing insights on the neural mechanisms underlying auditory cognition and
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for conditions such as otosclerosis. The position requires expertise in medical image analysis, proficiency with neural network architectures (particularly CNNs for segmentation tasks), and experience processing
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of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard
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researcher to investigate the neural mechanisms underlying decision-making, using the fruit fly Drosophila melanogaster as a model system. Funded by the BBSRC, this project will combine innovative behavioural