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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected to the project are: PhD Student Position in Generative
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qualifications Marine biogeochemical processes Hydrodynamic processes related to ships, turbulence, or mixing Oceanographic modelling Data analysis and programming (e.g., MATLAB, Python, or R) Interdisciplinary
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems
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Experience with performing laboratory experiments Ability to work with large data sets (> 500 GB) Numerical modelling Main responsibilities Independent research and research training (80% of time) Support for
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failures. We offer access to unique experimental data and computational tools developed by our research team for addressing a timely societally relevant problem. Project overview The aim is to unravel
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(advantageous but not required). Familiarity with using digital technologies in construction, like BIM (Building Information Modelling). Main responsibilities Participating in the Graduate School of Civil and
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of the numerical methods behind CFD and turbulence models. Experience in analyzing CFD data and interpreting simulation results. Excellent command of written and spoken English. Experience writing scientific reports
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(masterexamen) of 120 credits or a Master’s degree (magisterexamen) of 60 credits in Electrical Engineering, Communication Engineering, Engineering Physics, Computer Engineering or similar, with a strong
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long-term, and most often global, perspectives on future renewable fuels for transport. We seek to rigorously analyse the feasibility of energy transitions, utilize empirical as well as estimated data