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. Ideal candidates should possess a strong background and experience in computational modeling of solid-solid, liquid-solid, and gas-solid interfaces using first principles methods, classical force fields
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power. The work involves quantifying conflicts and synergies between different sustainability goals in the forest landscape using simulation and optimisation. The analysis will be based on spatially
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closely related discipline. Strong background in modeling and simulation of thermal/energy systems, preferably including PV/T or hybrid solar systems. Hands-on experience with thin-film deposition
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Integrating cross-disciplinary research related to the storyline from several work packages, research groups and institutions of NCCR CLIM+, ranging from natural climate models to the analysis of impacts
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security
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. If this hypothesis will be confirmed, it would mean that several independent baselines should be included in the isotope flux models, even for a single ecosystem. The geological actualism approach in this project is
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
you for multiple applications in Zintellect, they must click the unique link in each email request, but will be given the opportunity to copy over what they had previously submitted. All documents must
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, addressing issues such as non-determinism, continuous data dependencies, testability, and maintainability. Different strategies for evaluating architectures, including but not limited to simulation, best
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to lanthanide molecular magnets. While the SFX2C-1e model has been recently used to model core-level spectra, its combination with EOM-CC for molecular magnets has lagged behind. We will link EOM-CC to SFX2C-1e
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different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement learning, as