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The post holder will develop computational models of learning processes in cortical networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity
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regenerative medicine, biosensing, and therapeutics. Learn more at www.stevensgroup.org . This post is available from October 2025 for a fixed-term of 18 months, with the possibility of part-time appointments
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contributory pension scheme • 38 days annual leave • A comprehensive range of childcare services • Family leave schemes • Cycle and electric car loan schemes • Employee
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June 2026 subject to funding partner decision. The project will focus on scaling-up development our computer vision software for quantitative microscopy, Deep Learning for Time-lapse Analysis (DeLTA
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to learn and develop new experimental, computational and wet-lab techniques and approaches. • Identify genetic reagents and design experiments that test circuit function. • Construct
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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An exciting opportunity has arisen for a talented postdoctoral researcher to join the Oxford Machine Learning in Neuroimaging (OMNI) lab, led by Professor Ana Namburete, in collaboration with
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and motivated candidates for a postdoctoral positions working on cutting-edge research at the intersection of Machine Learning, Privacy-Enhancing Technologies, and Public Interest Technology. We
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programming), (2) populating agent-based models with realistic agent behaviours (e.g. using machine learning techniques), (3) calibrating large-scale agent-based models and (4) validation and verification
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning