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research experience in the mathematics of machine learning. Review of applications will begin immediately and continue until the position is filled. BACKGROUND CHECKS/CLEARANCES Employment with
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-Machine Interfaces (eHMIs) can enable safer and more inclusive interactions. You will: Develop a theoretical framework for identifying key characteristics of AV–VRU interactions and define design criteria
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optimisation tools for aperiodic lattice metamaterials. The research will integrate physics-based modelling with machine learning to enable the efficient exploration of vast design spaces defined by
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. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
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mathematics, machine learning, photonics, and clinical practices in vision. Be part of a multidisciplinary research team spanning science and engineering, psychology, and healthcare. Access state-of-the-art
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AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics, machine learning, and photonics. Be part of a multidisciplinary research team spanning
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measurement methods. These procedures target the assessment of steel properties for reuse in a new construction. Besides experimental work in the laboratory, machine learning will be employed to develop
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PhD in Computational Simulations of Turbulent Reaction Flows for Clean Energy and Sustainable Propul
machine-learning methods, you will analyze flame-turbulence interactions, pollutant formation, as well as unclosed terms relevant to LES modeling. The analysis involves the fluid dynamic as
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a mixture of computational, analytical and machine learning approaches to model the heat transfer to fuels and their physical and chemical behaviour, including changes in chemistry and physical
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selection criteria Knowledge/experience with control engineering, information fusion and/or data assimilation, marine technology Knowledge of and hands-on experience with machine learning and/or statistical