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Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs will also be provided Overview Edge artificial intelligence (Edge AI) enables deploying AI algorithms and models directly on edge devices. However, AI...
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only an upper bound on the relative of number of manipulated sensors and actuators, and without knowing their identities. With distributed Critical Infrastructures (CIs), event-triggered frameworks
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SPEAR Centre: PhD in ‘Long-Range, High Bandwidth Distributed Acoustic Sensing for Fibre Optic Links’
Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description SPEAR Centre: PhD in ‘Long-Range, High Bandwidth Distributed Acoustic Sensing
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Engineering Technology & Industrial Distribution Location College Station, TX Open Date 10/7/2025 Position ID 170945 Description The Department of Engineering Technology and Industrial Distribution
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Engineering Technology & Industrial Distribution Location College Station, TX Open Date 9/26/2025 Position ID 170273 Description The Department of Engineering Technology and Industrial Distribution
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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
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of conservation concern. In this conservation science PhD, the candidate will explore the role of land use and its management, climate, weather on the spatial distribution of protected waterbirds. The project will
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classification algorithms need to be extended with the capability to detect out-of-distribution environments and to autonomously infer their traversability. In this thesis, you will design a semantic
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distributed WaRM experiment (for details see here: https://onlinelibrary.wiley.com/doi/10.1002/ece3.9396). The employment is fulltime for three years. The deadline for applications is March 26, 2026 and the
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In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference