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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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to altered patterns of gene expression. We have assembled a unique data set of three independent lineages of Italian sparrow Passer italiae and their parental species, with data on gene expression and tissue
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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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gathering knowledge about the diverse physical and geometric properties of objects and dynamic changes in the environment. This involves leveraging rich sensory data—such as vision and touch—encoding
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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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consist of the following: Mathematical analysis of ecological and eco-evolutionary models, involving pencil-and-paper calculations; Computer simulations of more complex models which do not easily lend
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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