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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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field in hand by May 2026. Preferred Qualifications: Experience in Thermal Spray Materials/Processes/Characterization/Modeling. Experience in Solid Mechanics/Thermomechanics/Finite Element Analysis
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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches
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, and can analogous mechanisms be engineered into multi-agent AI systems? You would answer this question by building and testing computational models, developing multi-agent simulations where agents
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, while accounting for dynamic and stochastic demand patterns. The project addresses these challenges through a combination of advanced optimization methods (e.g., flow-based models that strengthen
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years the atmospheric modeling community achieved major advances in its capability to numerically simulate iodine chemistry in the atmosphere. This project will take advantage of the 1D chemistry and
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support research in: • Transportation systems modeling and simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling • Large-scale computing, cloud-native analytics
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Project: Enhancing APEX for simulating environmental and economic impacts of CAFO operations using the APEX model, funded by USDA-NRCS. Texas A&M AgriLife Research is seeking a highly motivated and
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support the development of electron and hole spin qubit devices based on Si/Ge heterostructures through analytical modeling as well as advanced numerical simulation with TB_Sim. Topics of interests include
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to lead an investigation exploring the ability of recently developed global earth system models to simulate coastal sea level across sub-annual timescales. This work will leverage a suite of coupled models