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of spectroscopic measurements. Investigate underlying receptor-analyte interactions with the aid of in silico calculations, including density functional theory (DFT) simulations. Apply machine learning approaches
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closely related discipline. You have a strong background and prior experience in atomistic and molecular simulation techniques, specifically density functional theory (DFT) and molecular dynamics (MD
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Theory (DFT) Microstructure prediction and evolution Secondary expertise in the following topics is desired but not required: Molecular dynamics Materials informatics Machine learning and artificial
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predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon suppresses LCI, including its effects on grain boundary site competition and the
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development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow
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health products, e.g. toothpaste. The simulations will utilize molecular dynamics and first principles DFT codes to model enamel surfaces and their interactions with the environment of the mouth. You will
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 20 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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, and 2 Research Platforms, covering different areas of nanoscience and nanotechnology. Job Title: Research Assistance in Spin orbit torque Department: Theoretical and Computational Nanoscience
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; previous experience as local contact at synchrotron XAS beamlines; knowledge of advance characterization techniques like XANES simulation and ab initio calculations (e.g. DFT). Good time management skills
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that can be benchmarked through physical experiments or computational simulations. The project is funded by the Research Council of Norway and is a collaboration between NTNU and Norsk Regnesentral