263 parallel-computing-numerical-methods positions at University of Groningen in Netherlands
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Intelligence with the topic “Derivation and Analysis of Free-Surface Flow Models”. The candidate would become a member of the Computational & Numerical Mathematics group of the Mathematics Department and will
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criminalisation of homelessness in other nations. The PhD researcher will be expected to employ doctrinal legal research methods as well as conduct a comparative analysis between jurisdictions. Depending
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researcher will be expected to employ doctrinal legal research methods as well as conduct a comparative analysis between jurisdictions. Depending on the project’s development and feasibility considerations
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communication skills are encouraged to apply. A MSc degree (or equivalent) in Mechanical Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic
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Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic modelling of materials and machine learning. Experience in atomistic modelling (molecular
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by drift-diffusion will be treated at the atomistic scale and vice-versa. A deep understanding of device physics, numerical modelling, and computer programming are, therefore, required. The PhD student
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, also in relation to specific computer hard- and software. Monitoring and updating hardware/software for observational education. Staying up to date with programming and observational methods in
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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for realizing challenging asymmetric catalytic methods. This network brings together academic research groups with expertise in experimental catalyst development and theoretical groups skilled in computational
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modelling. experience with computational methods in social sciences. experience with spatial and regional data. experience with handling large datasets. experience with the use of social survey microdata