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of experimental studies and numerical simulations.Identification and assessment of safety related issues during in-situ handling of the biocarbon may be done through methods like inspections
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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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demonstrate strong theoretical and methodological capacities as well as documented expertise in computational methods. Experience with high performance computing is strongly preferred. Experience from applied
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contribute to the current teaching needs of the Faculty of Law, including the multidisciplinary master program in human rights . The purpose of the fellowship is research training leading to the successful
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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
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in-depth qualitative analyses but also mixed-methods approaches, possibly enabled by emerging AI-enhanced techniques. The PhD project should overall contribute to a better understanding collaborative
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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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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