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Field
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application! We are looking for a PhD student in AI and machine learning with a focus on generative machine learning methods for cyber security applications. Your work assignments The primary focus of this PhD
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to design targeted marketing campaigns. Causal Machine Learning is an emerging research field that can learn the causal effect of an intervention and how it varies within a population based on a large set of
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on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
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automata, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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) Interpretable machine learning for network adaptation. In this thesis, the student will study how interpretable models and explainable learning algorithms could be used in real cellular networks for safe
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. - Apply graph-based and machine-learning tools to model signaling networks driving plasticity. - Validate candidate pathways in cell culture and mouse models. - Present findings at seminars and conferences
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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models