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Fully Funded PhD Studentship (UK Students Only) Real-Time Sub-THz Electromagnetic Sensing and Machine Learning for Dynamic Particulate Characterization University of Birmingham with support from
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modern machine learning, statistical signal processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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of mill and production operations. The scientific challenge will be to use the model and machine learning alongside live mill data (temperature, rolling loads etc) to reverse engineer the current
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insights for streaming, broadcast, accessibility and media production. Candidate profile Applicants should have a background in machine learning, audio engineering, speech processing or a related discipline
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systems are generally ill-conditioned. The project sits at the intersection of classical numerical analysis, scientific machine learning and computational chemistry. Based on regularization techniques and
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theoretical modelling. The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually ensure
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, a strong degree in computer science, cybersecurity, mathematics, or a related subject. Experience with cryptography, machine learning, or systems implementation is valuable, as are solid programming
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, machine learning, or NLP Published work in reputable conferences or journals Outstanding academic performance in relevant modules or degrees A strong motivation to work on cutting-edge research in Agentic
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and