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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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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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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