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hub dedicated to advancing, implementing, and disseminating state-of-the-art applied economic problem-solving informed by data analytics solutions. The successful candidate will develop a strong applied
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include: Knowledge of fault mechanics Experience in physics-based numerical modelling Background in structural geology or tectonics (especially crustal fault in South America) Familiarity with high
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proficiency in modeling to assess how post-wildfire impacts soil functioning (infiltration, runoff, strength, composition, ecology), smoke and pyrometeor deposition, soil carbon and nutrient cycle alterations
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distribution systems. Current research initiatives include developing advanced vibration analysis methodologies for wooden packaging systems, predictive modeling for packaging performance, and optimization
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Postdoc position to support international research and capacity-building projects employing elect...
ground-based transient electromagnetic (TEM) methods. The position is part of a Danish Ministry of Foreign Affairs funded project titled “Scaling Up Electromagnetics Surveys for Large-Scale Hydrogeological
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: Beijing, Beijing 100190, China [map ] Subject Areas: Nuclear Physics / hadronic structure; Nuclear structure; Lattice QCD; , Heavy-ion collisions , High-Energy Physics , High-Energy QCD, Parton Saturation
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on improving the productivity of Opuntia (cactus pear) as a sustainable bioenergy feedstock through integrated genomic, metagenomic, and systems-level modeling of plant–microbe–soil interactions. This position
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developing countries, and a potential for a new future crop in Scandinavia. Using sweet potato as a physiological model, the research will identify key metabolic bottlenecks in the plant’s source–sink
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(AI), mechanistic modeling, and quantitative systems pharmacology (QSP). Funded by an NIH R01, our group develops predictive frameworks that integrate advanced AI/ML methods with multiscale mechanistic
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University of Southern California (USC) | Los Angeles, California | United States | about 2 months ago
quantitative systems pharmacology (QSP). Funded by an NIH R01, our group develops predictive frameworks that integrate advanced AI/ML methods with multiscale mechanistic models of disease biology and drug action