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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing
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experts to acquire bespoke training and testing data; develop prototype solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated
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objects such as neutron stars and black holes. Strong computational experience and proficiency in simulations is highly preferred. The post-holder may be expected to help in the supervision of DPhil and
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trapping or fluorescence microscopy to study DNA replication; • develop and employ simulations and data analysis routines to analyze your data; • develop an interdisciplinary skillset by
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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developing mathematical algorithms and simulations in MATLAB, in particular with Semidefinite Programming and Sum of Squares and of the analysis and design of feedback control systems using these approaches
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candidate will be part of a research group with responsibility for carrying out research in Modelling, Simulation, and Analysis of Interacting particle systems and related fields as part of the Royal Society
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necessary for the benefit of the project; • employ simulations and data analysis routines to analyze your data; • help to establish a scientifically outstanding and warmly communicative
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mathematics or a related discipline, and possess sufficient specialist knowledge in the discipline of theoretical mechanics, mathematical modelling, and numerical simulations of PDE to work within established
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experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools; Code synthesis