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distribution and transmission network in Australia present a potential solution. This network includes 42,000 km of high-pressure low-carbon steel pipelines for transmission and 97,000 km of low-pressure
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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membership is an EMBL Partnership Programme established in 2010, entitled ‘EMBL Australia Partner Laboratory Network’ (PLN). The partnership aims to seed a dynamic, highly collaborative culture across
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venues, and network with the international research community. Requirements: very good research-oriented university degree in a discipline that is relevant to SECAI’s research fields (e.g
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Full-Time Regular/Temporary Regular Job Code 9403R Employee Class Instr Fac/TC and Coord Cmp Add to My Favorite Jobs Email this Job About the Job We are seeking a dedicated and accomplished individual
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researcher with expertise in communication, project management, and leadership. You will build a robust national and international network and acquire advanced knowledge essential for implementing critical
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, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs) to improve power efficiency and preserve power integrity. Integrated voltage regulators (IVRs
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Background Network Rail operates several telecom networks which provide connectivity for various signalling systems. Therefore, the performance of telecoms assets is integral to how the railway system operates
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to coordinate and conduct asset management and maintenance activities across infrastructure networks in the Netherlands. Together with our research partners from Next Generation Infrastructure network, the PhD
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key