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Field
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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glaucoma (POAG) is the leading cause of irreversible blindness worldwide. It arises from fibrotic remodelling of the trabecular meshwork, a filter-like structure in the anterior chamber of the eye. As the
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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the Department of Biomedical Engineering at Swansea University, but you will also interact closely with our national network of clinicians from across the UK. This ensures the project stays grounded in clinical
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International applicants – January 2026 PhD: Applicants for PhD must hold an undergraduate degree at 2.1 level (or non-UK equivalent as defined by Swansea University) in Engineering or a similar relevant science
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of network defenders, software developers and vendors, security researchers, AI practitioners, and policymakers. You'll get a chance to work with elite AI and cybersecurity professionals, university faculty
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University of California, Los Angeles | Los Angeles, California | United States | about 20 hours ago
prohibiting sexual harassment, sexual assault, or other forms of harassment, discrimination, dishonesty, or unethical conduct, as defined by the employer. UC Sexual Violence and Sexual Harassment Policy UC Anti
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with a wide network of stakeholders, and explore new avenues for medical applications. For ongoing work and publications on this project, please see our website: www.cnnp-lab.com . This is a 12-months
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system