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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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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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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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(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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, patents, journal papers, and conference publications, participate in proposals, and estimate costs. Basic Qualifications: A PhD degree in physics, optical or electrical engineering, nuclear engineering, or
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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
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a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in materials science, nuclear, or mechanical engineering, or a related
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Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: Candidates must have a PhD in nuclear
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fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, computer science, or a related field completed within
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interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Basic Qualifications: A PhD in Earth and Environmental Sciences, Ecology, Biosciences, Botany, Geosciences, or a related