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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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(transient loads up to ~1 GW/m²). Computational modelling tools will complement the experimental work to guide joining strategies and post-weld heat treatment protocols. The outcomes will support selection
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that simulates real-world environmental conditions to test the durability and longevity of these materials and products made thereof, is also required. This PhD project aims to investigate novel high-performance
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team bridging aerospace and materials research, with access to high-performance computing resources and the university’s state-of-the-art materials research facilities, including high-resolution X-ray CT
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the development of system software. Key questions include how LLMs can support programmers in writing complex logical code, generating high-quality tests, and optimizing performance. Moreover, when integrated with
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spatiotemporal coupling (STC) at the far-field Design optical elements to generate a specific RDG and STC, e.g. doublet lens including axi-parabola Perform laser wakefield acceleration using "flying focus
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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PhD fellowship in Extended Reality for Behaviour Change The Pioneer Centre for AI and the Department of Computer Science at the University of Copenhagen invite applications for a fully funded PhD
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into practical performance solutions. The “Travel, sleep and fatigue optimal management for long-haul performance” PhD programme will focus on mitigating circadian and travel fatigue challenges associated with
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physics and diagnostics at the York Plasma Institute Computational combustion modelling using High-Performance Computing (HPC) Machine learning techniques for predictive combustion models Research