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strong background in circuit neuroscience but will also consider scientists with a strong computational preparation and some neuroscience background. Prior experience with connectomics data
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for Neuroimaging Science at King’s College London based at Institute of Psychiatry, Psychology, and Neuroscience (IoPPN). The IoPPN promotes cutting-edge research and translation into practice, addressing a wide
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Neuroscience (IoPPN). The centre aims to better understand the complex interrelationships between society and mental health, with a commitment to: Ensure that the impact of social context is central to how we
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and PhD students) and openly share expertise knowledge with team members (e.g. Post-docs). Collaborate with other two post-docs working in this research programme, to deliver on project aims. Contribute
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neuroscience, with the aim of improving mental health outcomes in both non-clinical and clinical populations. We are based at the MRC Cognition and Brain Sciences Unit, University of Cambridge, a world-leading
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schizophrenia-related symptoms in animal models (mice), in the context of a collaborative project with clinicians and computational scientists. This project will be supervised by Prof Oscar Marin and Prof Beatriz
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clinicians and computational scientists. This project will be supervised by Prof Oscar Marin and Prof Beatriz Rico. Candidates should have strong experience in stereotaxic surgeries and Electrophysiology
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neuroscience or a related field. Experience of MEG/OPM-MEG acquisition and data analysis is essential. Experience with MATLAB or Python computer programming for neuroimaging data analysis is essential
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consists of several post-doctoral and PhD students with backgrounds in psychology, cognitive science, neuroscience, computer science, engineering, and physics. The focus of our research is to develop new
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to the regulation of complex behaviours. This will involve a range of techniques including high resolution confocal microscopy to determine receptor localisation, behavioural analysis of C. elegans and computational