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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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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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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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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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graph neural networks for complex sensor networks such as those involved in brain imaging Develop and test data-driven methods for image and video processing for microendoscopy. Key Duties and
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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
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neural network models, produce stimuli for artificial and biological agents, participate in experiments with chicks maintained in the Biological Services Unit, contribute to lab meetings and research
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well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research directions relevant for this position. A high degree of self