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and theory-guided machine learning algorithms for the prediction of manufacturing processes in composite materials. Development of user subroutines for finite element constitutive models Validation
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be defined at two levels: SAACD Component: This is a UAV made up of hardware and software sub-systems, capable of observing, predicting, deciding and reconfiguring itself to fulfil its mission (e.g
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machine learning algorithms for the prediction of manufacturing processes in composite materials. Development of user subroutines for finite element constitutive models Validation of model and numerical
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traditional (hydrothermal quartz, barites etc.) paleo-atmospheric proxies via noble gas and noble gas-nitrogen static mass spectrometry. The main goal of the project is to identify new geological areas as
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Supervisor: Professor Fernanda Duarte Start date: 1st October 2026 Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand
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mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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Requirements Knowledge: Design and calculation of machines. Finite elements. ICT. Professional Experience: In the design of machines, preparation of documentation for manufacturing and production. Electrical
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computing; • show the ability to conduct independent research and to collaborate within an interdisciplinary team. Experience with open-source finite element software (e.g. FEniCSx or similar) and/or machine
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the project Con-TEki investigates the role of transposable elements (TEs) as drivers of regulatory innovation in conifers (Norway spruce and Scots pine), contrasting outcomes with an angiosperm (aspen
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8 Jan 2026 Job Information Organisation/Company Universidade de Vigo Department T03 Mechanical Engineering, Machines, and Thermal and Fluid Motors Research Field Engineering » Mechanical engineering