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University of the Arts London, London College of Communication; | London, England | United Kingdom | 4 days ago
The opportunity Join University of the Arts London (UAL) and play a vital role in shaping, supporting and coordinating Digital Learning activities within London College of Communication (LCC) and
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary CNC machining delivers high precision but is costly, rigid, and limited in adaptability. Robotic
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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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The always-on, safety-critical nature of air traffic control raises rich and exciting challenges for machine learning and AI. The University of Exeter in partnership with NATS, the UK’s main air
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PhD studentship: Adaptive Logic-Based Machine Learning Grids Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs
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an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
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Deep Learning, Neural Networks, and Machine Learning. Experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow, Keras). Strong commitment to teaching excellence and student-centered
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reservoirs. By embedding governing equations and boundary conditions directly into machine-learning models, the project aims to enable efficient exploration of high-dimensional parameter spaces without
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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prototyping with access to Lancaster's high-performance computing facilities. Essential Requirements • PhD in Machine Learning, Computer Science, Computational Neuroscience, or related field