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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and
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at the intersection of applied mathematics, computational science, and high-performance computing. The successful candidate will help develop advanced scientific computing methods and deploy them on state-of-the-art
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or related Applied Economics discipline with a strong quantitative focus. Demonstrable knowledge of applied econometrics and statistical evaluation methods, such as: Difference-in-Differences (DiD
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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal
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research on understanding condensed matters using neutron scattering. Major Duties/Responsibilities: Develop new sample environment at various neutron beamlines Perform pump-probe-enabled time-resolved
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related field completed within the last 5 years Good track record in scattering theory, quantum many-body theory, thermodynamics, statistical mechanics, or non-equilibrium physics. Experience in
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and