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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
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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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methodological capacities as well as documented expertise in computational methods. Experience with high performance computing is strongly preferred. Experience from applied work in change and anomaly detection is
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level subject/master’s degree worth 120 ECTS in health sciences, kinesiology, or similar excellent working knowledge at master’s degree level of quantitative research methods and statistics experience
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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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development of computer systems for data analysis, development of machine learning methods, and the clinical use of technology. Within the research groups you will therefore work together with computer
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-prediction benchmark studies. Depending on the qualifications and preferences of the candidate, the work may entail experimental investigations and/or modelling in the open-source computational fluid dynamics
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emissions and economy, there is limited knowledge on how this will affect the rate of degradation of the road infrastructure. You will conduct laboratory investigations, possible field work and numerical