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Join the Oxford Martin Programme on Forecasting Technological Change at the University of Oxford, led by Dr François Lafond, Prof J. Doyne Farmer, and Prof Max Roser. This pioneering programme aims
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of equilibrium dynamics using tensor networks to solve partial differential equations, with applications to many-body quantum systems, as well as computational fluid dynamics and plasma dynamics. The successful
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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reactions. The work will extend ChemZIP to handle catalytic chemistry, develop a reduced-order 1D network model linking reaction and pressure-drop effects, and use it to optimise catalytic structures within
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out rigorous and impactful research into the computational mechanisms of human learning using deep neural network models, and disseminating the findings within the research group, across the wider
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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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leader Pedro G. Ferreira and other members of the Beecroft Institute of Particle Astrophysics and Cosmology. The post holder will be a member of a disparate research network working independently to carry
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Raman’s cardiovascular research team. This role is embedded within a cutting-edge programme focused on integrating high-dimensional datasets, including advanced cardiac MRI (oxygen-sensitive, metabolic, and
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and inclusive culture. Diversity is positively encouraged, through our EDI Committee, working groups and networks, for example eng.ox.ac.uk/women-in-engineering, as well as a number of family friendly
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Computational Neuroscience and related fields as part of the Medical Research Council, UKRI grant “Algebraic topology bridging the gap between single neurons and networks”. They will be expected to conduct