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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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on agentic approaches, where an LLM interacts with visual tools, which may themselves be neural networks. Central challenges include enabling LLMs to reason about visual structures, designing
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convolutional/neural networks Experience with explainable and interpretable AI (XAI) Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University’s rules
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the past ten years thanks to artificial intelligence, mainly in the form of deep convolutional neural networks. In parallel, functional analysis of tissue samples via novel microscopy techniques and spatial
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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are unpredictable, e.g., unanticipated changes in the environment of the ACPS may cause a neural network to produce faulty outcomes that could endanger the safety of the system. To assure the safe and reliable