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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies
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, particularly turbulence in the boundary layer; developing new high-speed measurement techniques, particularly using optical diagnostics methods; augmenting experimental data with Computational Fluid Dynamics
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to apply. The position will be under the direction of Professor Anthony Yeates and will primarily involve optimisation of a numerical magnetic field model for the global solar magnetic field against
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address these issues by developing mathematical foundations for XAI, and proving performance guarantees for new explanation methods using the same standards as in other parts of machine learning theory. As
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of wave equations. Proficiency in numerical implementation and computational methods will be appreciated. Candidates with a PhD in semiclassical analysis who are keenly interested in numerical analysis
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, or similar disciplines Graduate students expecting to receive their PhD within six months can also apply Experience in the advanced analysis of genetic or proteomic data Interest in learning methods
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, or a related field with a focus on groundwater or hydrological modelling Documented experience in numerical or data-driven groundwater modelling (e.g., MODFLOW) Proficiency in handling geospatial and
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. Fort Collins is situated on the Front Range of the Rocky Mountains with views of 14,000 foot peaks, and offers access to numerous cultural, recreational, and outdoor opportunities. The Department
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image quality in time-resolved micro and nano CT data. Develop and apply numerical implementations in simulation studies as well as to large experimental micro and nano CT data. Explore multi-modality
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computing (HPC). This position offers an exciting opportunity to work on cutting-edge research projects at the intersection of numerical linear algebra and advanced HPC. The candidate will join an