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This project focuses on the development of quantum–classical modeling strategies for multiphase flow systems. The PhD topic is on exploring how emerging quantum computing methods can be integrated with classical
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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. Under this job title, you will develop a CFD modelling platform to evaluate the combustion, flammable and toxic gases emission and dispersion characteristics resulted from battery fires/thermal runaway in
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Number AE2025-0514 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0514.pdf CALL FOR GRANT
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-cases of classical supercomputers, the development of quantum CFD algorithms will be of widespread benefit upon the arrival of fault-tolerant quantum computing. This project involves the adaptation
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develop an algorithm for gas emission localisation and quantification using advection-diffusion models, CFD, and Navier-Stokes equations. Working alongside Durham. Key tasks include: · Integrating
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focus on translational Research, Development & Deployment which focus on specific area of the energy value chain, and a number of Living labs and Testbeds which facilitate large scale technology
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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical
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parameters, optimising these and various process conditions to enhance the overall performance of the gas turbine employing specific indicators. Used with a complementary CFD modelling approach, the research