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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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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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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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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