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relies on combining polarized quasielastic neutron scattering (pQENS), inelastic neutron scattering (INS), and molecular dynamics with machine-learned potentials to gain insights into ion conduction
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will also be encouraged to take part in the supervision of MSc and PhD candidates. You will be part of a larger project group across robotics, machine learning and health, and also interact with
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to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to what extent simpler models (possibly based on machine learning) can be
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26th July 2024 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Compiler Technologies Apply for this job See advertisement This is NTNU NTNU
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-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to what extent simpler models
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combination of geophysical, geological, and petrophysical data with advanced data processing techniques, including multi-component elastic full-waveform inversion, AVO inversion and machine learning, will be
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for feature extraction, which can serve as parameters in simulation. Alternatively, machine learning methods can be employed for comparison with the primary analysis-based approach. The objectives of this PhD
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fields both locally (field by field) and regionally (groups of fields). Data driven analyses will be complemented by physical reservoir modelling, with machine learning approaches to extract correlations
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the field of Computational Material Modeling, related to the “Designing Solid Electrolytes for Li- and Na-ion Batteries using Deep Learning-based Models (DeepSolo)” project. The PhD candidate will be part of
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optimisation of aluminium extruded crash management systems (CMS). This research project combines operations research methodologies with machine learning techniques to revolutionise the design process, ensuring