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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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to single-platform radar systems, distributed Synthetic Aperture Radar (SAR) architectures offer increased flexibility and resilience, more rapid environmental mapping, improved spatial coverage and enhanced
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qualification/experience in Robotics, Engineering, Computer Science, Maths or closely related discipline (e.g. social science applied to technology). Expertise relevant to one of these areas: distributed AI
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We are seeking to recruit a Postdoctoral Research Associate fixed term until November 2027. The Problem: Understanding what sets the spatial-temporal distribution of plankton in the ocean is
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the wind/renewable energy training and research elements of the CDT programme. Funded by ESB and EPSRC, this 4 year this PhD studentship, at the University of Strathclyde is in the area of economics and
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. This paves the way for the application of MPC to large-scale systems, since the computational bottleneck is removed. The basic challenge is how to coordinate the distributed decision making of agents so that
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Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: PhD in ICSA with a start date of 1 September 2026. Applicants should state “Memory
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more efficient by identification of the critical processes and parameters. In this project, we aim to develop mathematical and computational models to predict the spatial distribution of concentrations
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accessible measure of location. One option is to use skew versions of know distributions that leave the mode or median of the distribution at the origin. The main objective of this project is to explore, from
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practice offers no robust, scalable, or provable mechanism to guarantee this right once a model has been trained. The distributed nature of federated settings introduces unique challenges for unlearning