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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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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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data-driven production planning affect industrial sustainability—from economic, environmental, and social perspectives. Your work assignments: Conduct life cycle cost analyses (LCC) to assess
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departmental duties, up to a maximum of 20% of full-time. Your qualifications We are searching for someone with a Master’s degree in computer science, media technology, computer engineering, human-computer/robot
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mathematical modelling and data science with diverse disciplines, including ecology, plant physiology, and molecular biology. Your research will deepen our understanding of how living systems respond to stress
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into how algorithmic systems influence the circulation of information and disinformation across digital platforms, and how such processes affect perceptions of credibility, truth, and democratic
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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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to work at the forefront of multidisciplinary science, integrating mathematical modelling and data science with diverse disciplines, including ecology, plant physiology, and molecular biology. Your research
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application! We are looking for one or two PhD students in Multiagent Automatic Control at the Information Coding Division (ICG), which is based at the Department of Electrical Engineering (ISY). You will be
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involves collecting clinical data on the effects of childhood cancer treatment, bioinformatically handling sequence data and developing prediction models, as well as conducting Single Cell RNASeq studies and