12 finite-element-analysis uni jobs at King Abdullah University of Science and Technology
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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
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-chemical characterization: elemental analysis, CEC, pH, particle size, surface area, moisture release curves, hydraulic conductivity, etc. Phenotype plant traits to pinpoint the effects of various factors
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at the water–soil–plant interfaces of food crops and native trees in alkaline sandy soils. We invite agronomists, soil specialists, irrigation specialists, and experts in drylands agriculture to apply to broaden
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. This includes facies analysis, environmental research, diagenesis, pore-network analysis and stratigraphy. Additional ideas will also be considered. A multitude of data is already available including shallow
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of emergency management, mitigation, preparedness, response, and recovery, and leads critical operational functions such as hazard vulnerability analysis, scenario planning, drills, interdepartmental
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in simulation, prediction, and analysis of large-scale and complex fluid systems. Special emphasis will be directed toward incorporating high-performance computing, advanced algorithms, machine
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-of-the-art laboratory using a wide range of analytical techniques, related sample preparation, statistical analysis and data interpretation. The successful candidate will be in charge of operating high
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of microbiota in human health and disease, aiming to identify novel microbiome-based therapeutic strategies. Expertise in functional and metabolic analysis of microbiomes using Omics technologies, studying
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limited to Applied Analysis, Partial Differential Equations, Numerical Analysis, Scientific Computing, and Optimization. Candidates with portfolios that align with Saudi Arabia’s national priorities
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Bioinformatics (generative protein design) Methodology (machine learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI