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Field
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analysis and extreme value theory, as part of the NWO-funded project “Seeing beyond the study duration: extreme value theory for long term survival and cure chances”. This project will develop statistical
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) into reliable and efficient decisions. The work spans theory to lightweight on-hardware prototypes, with publications targeted at leading IEEE venues in communications and signal processing, and relevant AI
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investigate how the strengths of modern machine learning can be combined with the rigorous foundations of control systems theory to create a new generation of intelligent underwater robotic systems
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process. This project offers a unique opportunity to work at the intersection of machine learning and control theory. You will develop rigorous theory and scalable computational methods for certifying
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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services
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into applications. TRILOGY brings together 11 leading institutions across Europe to train 16 Doctoral Candidates at the forefront of theory and experiment in ultrafast science. The programme offers a unique
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systems, but the field is broad and is related to many other areas of mathematics, including measure theory, number theory and algebraic and complex geometry. As a PhD student in this project, you will
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mixture of theory and practice. The selected students will be part of the Department of Computing Science at Umeå University, as well as the WASP graduate school. Possible research directions may be
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affecting lasting change, the faculty, students, and staff of HBHE work in the pursuit of an equitable, healthy future for all. Turning theory and research into actionable practice, we develop interventions
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these psychological constructs into formal computational rules suitable for agent-based modelling. The PhD student will ensure that formalisations remain faithful to the underlying psychological theory—a translational