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cutting-edge characterization facilities. One aspect of the SOLARIS Initiative and this position is the discovery of novel materials through combinatorial materials science and the optimization
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project on MATLAB, Python, Julia, Javascript experience in hydropower engineering with control engineering and optimization is a distinctive advantage possesses a strong academic record, with relevant
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Research Fellowship period at the University of Oslo. Jobb description The person hired in the position will work on theoretical control and optimization methods for a grid island powered by renewable energy
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position is the discovery of novel materials through combinatorial materials science and the optimization of their physical properties for solar energy conversion applications. In particular, material
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of black-box SAT- and ILP-solvers in combinatorial optimization, to the inherent dependence on empirical evaluation found in common machine learning pipelines. The goal of the project is to advance methods
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have the opportunity to travel to CERN for experiment building and beam tests for several weeks each year. The numerical simulation work will include modeling and optimization of beam optics, radiation
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envisioned to update iteratively as new data become available to optimally infer surface fluxes across the landscape. The work will build on and extend the existing infrastructure at the Department
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related to sample fabrication, oxide growth, and optical characterization, as well as fabrication of test structures. Relevant tasks may be a combination of: Optimization of SiO2 growth process on SiC
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research team that aims to develop novel digital twin solutions that help us make the world a better place to live by understanding and potentially tackling how pandemics evolve and how to optimize
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the environmental impact of hydro power plants in sediment laden rivers. This will be achieved by optimizing turbine designs to better manage high sediment loads and large sediment sizes, as