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join a highly interdisciplinary project at the interface of simulations and theory of porous materials, peptide self-assembly and adaptive materials. You will have access to state of the art
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presentation of results at international conferences and publication in peer-reviewed journals are also expected About you: Advanced knowledge in control systems and feedback theory Programming skills in high
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at international conferences and prepare scientific publications Your Profile Master’s degree in physics or a closely related subject Solid background in quantum many-body theory and/or statistical physics
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formation and microstructural evolution by molecular dynamic, kinetic Monte Carlo, and kinetic rate theory calculations will complement the experimental findings, in order to provide an improved understanding
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expected that the topic of the Master’s degree is connected to the academic field to which you are seeking admission. We can offer: Salary as PhD (code 1017) NOK 593 700, - per year. Automatic salary
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characterized to be far from thermal equilibrium, such that the governing physical principles have to be reconsidered. We combine modeling, high performance computing, and analytical theories tailored
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of the effects of land use and climate change on mountain ecosystems and landscapes Methodological knowledge for analysing social-ecological systems (e.g. Theory of Change, DPSIR model) and for analysing trade
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
of AI is both a technical and an integrative challenge. It requires combining what AI excels at, such as pattern recognition and processing data streams, with theories and methods for planning and
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of children in urban contexts. We are looking for a motivated PhD candidate who can conduct interdisciplinary research, combining theories and methods from urban studies and biomedical sciences. Researching
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coupled-cluster (EOM-CC) theory with periodic DFT (pbcEOM-CC). To quantify spin-phonon couplings of spins on surfaces—the bottleneck to modeling spin relaxation—we will develop a new approach using pbcEOM