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Field
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are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints. Existing predictive models provide useful but incomplete support because they cannot fully
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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National Aeronautics and Space Administration (NASA) | Merritt Island, Florida | United States | about 3 hours ago
involving environmental stress responses would also be beneficial. Skills in computational modeling, data science, or simulation-based research may support participation in activities that integrate genomic
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21 Aug 2026 Job Information Organisation/Company CNRS-LMA Department laboratoire de Mécanique et d'Acoustique UPR 7051 Research Field Mathematics » Computational mathematics Engineering » Simulation
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. Experience and expertise in human mobility simulation and prediction with agent-based modeling and deep learning techniques. Proficient in Python programming for geospatial data processing, modeling, and
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | 24 minutes ago
Materials. The learning objectives for this project are: (1) use atomistic simulation software and AI/ML techniques on sensing materials database development, (2) analyze the modeling results, and (3) learn
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simulations, enhanced sampling, and kinetic modeling. Research activities will examine molecular interactions involving neurotransmitters and hormones, including norepinephrine and estrogen, and their effects
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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-transcriptional gene regulation; kidney biology or polycystic kidney disease; mouse genetics, transgenic mouse models, and animal phenotyping; mammalian cell culture, primary cells, or kidney organoids; or RNA
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structuring of river systems. Familiarity with software and tools for simulating river network dynamics, such as R, Julia, Python, or GIS-based hydrological modeling platforms. Ability to integrate physical