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are optimized based on plant performance and energy price fluctuations. The learned models and resulting data-driven control approaches will be designed to be more easily transferable between different
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and expertise in energy storage technology. Student type Future Students Faculties and centres Faculty of Science & Engineering Western Australian School of Mines (WASM) Course type Higher Degree by
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experience in cleanroom microfabrication, actuator design and characterisation, finite element modelling, rheology, ex vivo tissue methods and cell culture. You will work across engineering and life sciences
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6 Sep 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Energy and Process Engineering Research Field Engineering » Process
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technologies based on a digital twin. The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining
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plant phenotyping, microbiome profiling, predictive modelling and targeted experimental validation to identify the microbial factors that make plants more resilient to disease. Your job You will join the
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biogeochemical processes that govern their behaviour. Job description The PhD candidate will develop and analyse new process-based mathematical models to improve our understanding and predictive capability
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programme Wireless Communications; as per January 1, 2027, or as soon as possible thereafter. In electronic engineering, Aalborg University is known worldwide for its high academic quality and societal impact
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. To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
knowledge of AI-enhanced planning in shipbuilding supply chains. Apply quantitative methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient