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process-based modeling of hydrologic or land surface processes. The WSMG group develops advanced surface/subsurface integrated hydrologic and reactive transport models, works with other groups to compare
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and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
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crystal material’s growth and characterization. You will perform cutting-edge research on theory and modeling of dynamics in condensed matter. Major Duties/Responsibilities: Development of theoretical
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics- models and/or physics-aware ML-models that can bridge
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, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows. Investigate strategies to balance performance
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
simulations. Design, develop, and validate physics-informed AI/ML models with features from electronic structure, spectroscopy to control materials growth and emerging functionalities. Develop and train agentic
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of existing frameworks and the development of new physics-based dynamic models. The candidate will lead the planning, coordination, and execution of experimental validation campaigns, ensuring strong
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Implement localization and triangulation algorithms using multi-sensor fusion (e.g., TDOA, AOA, RSSI-based methods) Develop RF propagation and digital twin models for infrastructure environments (urban
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and