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land loss and vegetation change at high spatial resolution Work closely with remote-sensing scientists, modelers, and empiricists across DOE laboratories and universities to address project objectives
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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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statistical mechanical theory. Collaborations with internal and external fusion efforts will be strongly encouraged. The successful candidate will be mentored and teamed with staff in the National Center
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recovery following hurricanes and other disturbances at scale. Apply explainable AI and statistical methods to identify physical, environmental, and biological drivers of ecosystem change and resilience
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mechanics, statistical modeling, and linear algebra Preferred Qualifications: Experience with density matrix renormalization group and tensor network algorithm development and application. Competency with
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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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theory, thermodynamics, statistical mechanics, or non-equilibrium physics Preferred Qualifications: Rich experience with transport measurements and characterization Basic knowledge of models of strongly
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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