36 molecular-modeling-or-molecular-dynamic-simulation positions at King Abdullah University of Science and Technology
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We invite applications for a faculty position in computational science and engineering with a focus on geophysics or fluid dynamics, as well as machine learning with one of the following experiences
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activities: · Understanding/simulating the diffusion paths and cavitation in thermoplastic liners. · Multi-physics simulation of the diffusion process in thermoplastic orthotropic composites
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innovative, technology-driven research in Infection Biology, Microbiomics, and Epidemiological Disease Modelling. Preference will be given to those with expertise and a proven track record in one
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state-of-the-art genomic technologies. We ask fundamental questions on how cells operate as molecular machines with special reference to the dynamic gene regulatory networks governing cellular identity
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seeking to expand research, higher education and innovations in the field of Marine Data Modelling and Integration. Applications for a faculty position at the rank of: Assistant Professor are invited from
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-inspired approaches for modeling, designing, and predicting the response of composite systems. Responsibilities: Develop AI approaches for predictive multi-physics response of composites in Energy
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling
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Modeling naturally fractured reservoirs is re-gaining interest in the Oil & Gas industry and academia for application in carbonate fractured reservoirs and unconventional reservoirs where natural
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molecular biology to address critical questions in genome biology, evolution, and disease resistance of wheat. Our mission is to perform impactful, curiosity-driven research that translates into crop
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is to develop a modeling framework including the use of Random-Walk method to predict NMR measurements, pore-scale finite-element modeling on 3D digital models, generated from CT-images to predict