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Do you have a strong technical background in Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols needed
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model and biological membranes. The experimental data will be paired with results from molecular dynamics simulations to provide a complete characterization of the biophysical properties of the imaged
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stabilising structures iv. AI-enabled structural analysis, optimisation and design tools for high-efficiency, low-carbon composite building systems Plan and conduct experimental work, including but not limited
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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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of group's research is focused on source apportionment of size-resolved AA based on mass concentration data, chemical composition and particle number size distribution. The application of receptor models using
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capabilities in modelling of thermal compositional multiphase flow and coupled process-structure interaction in fractured porous media. Qualifications and personal qualities Applicants must hold a Norwegian PhD
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of habitable conditions on orbiting planets. This project places the modelling of cool star high-energy environments at its core, developing a generalised framework for characterising time-resolved stellar
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Biotechnology and Bioprocesses, Membrane Technology, Chemicals and Materials, Resource Recovery, and Modeling & Artificial Intelligence. By harnessing its cross-cutting, interdisciplinary, and transformative