19 machine-learning "https:" "https:" "https:" "https:" "https:" "University of Cambridge" positions
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A Research Assistant position in Clinical Neuro-AI is available to work with Prof Zoe Kourtzi (Adaptive Brain Lab, University of Cambridge; https://www.abg.psychol.cam.ac.uk ) and Prof Eleni
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of Cambridge; https://www.abg.psychol.cam.ac.uk ) and Prof Eleni Vasilaki (University of Sheffield, School of Computer Science). Are you passionate about uncovering the brain mechanisms that support adaptive
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Postdoctoral Researcher in visual cognitive computational neuroscience Supervisor: Dr. Kamila Maria Jozwik, Jozwik lab, University of Cambridge Application deadline: 2 April 2026 Start date: October
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‑of‑the‑art computational featurisation with experimental reaction‑kinetics data to build a machine‑learning platform capable of predicting catalyst performance. This is an exciting, highly collaborative
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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experiences for leaders at all levels, working across industries to shape the future of leadership, innovation, and organisational performance. As part of the University of Cambridge ecosystem, we offer
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performance. As part of the University of Cambridge ecosystem, we offer an intellectually rich and collaborative environment, welcoming global participants and contributors who are shaping the future
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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The Fitzwilliam Museum: Opening up the past to transform our futures. As the principal museum of the University of Cambridge and the largest cultural venue in the region, The Fitzwilliam Museum acts
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) Neuromodulation approaches (TMS, tDCS, TUS) Neurogenetics Computational modelling (machine learning, reinforcement learning) Our research bridges scales (local circuits to global networks) and species (humans, mice