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applications in parametric modeling environments, including beam, shell, and solid-based elements. Creating a robust foundation in FEM theory and numerical methods, enabling the candidate to specialize in
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of teaching and administrative duties (up to 25 %), depending on the competence of the successful applicant and the needs of the department. The research fellow must take part in the Department’s PhD program
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to the ph.d. program Strong background in quantitative methods (reflected in courses and/or research experience) Proficiency in R, Python, or similar programming languages (or strong skills in another
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on theory, methods and applications. The areas represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations
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Ability to actively communicate and co-operate within a larger research team is required. Experience with LINUX environments and analysing large datasets from numerical models is an advantage Experience
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employment that the PhD Research Fellow is enrolled in USN’s PhD-program in PhD in Technology within three months of accession in the position. It may be possible to get a four-year full-time period consisting
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a single method for anisotropic flow modelling for both ice and olivine, by mapping CPO parameters directly to anisotropic viscosity parameters. This technique should reduce the computation complexity
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methods to be considered for numerical optimization by an Energy and Emission Management System (EEMS). Data-driven AI methods (e.g. Reinforcement Learning and/or Recurrent Neural Networks) to be considered
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have expertise in at least one of the following research areas: PDEs, numerical methods, optimization, functional analysis, or stochastic analysis Candidates without a master’s degree have until 1st