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Field
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the mobility, ubiquity, security, and interactivity of computers, data, software, and users. The pervasive computing paradigm enables technologies such as sensors, actuators, and computers to take a back seat
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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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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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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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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
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to act in accordance with the university’s policies on equal opportunities and data protection. Other Qualifications You must have at least 240 higher education credits (ECTS), of which at least 60 credits