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recover from disruptions, as well as the risk of cascading interactions between them. Key responsibilities: analysis of the planetary-scale resilience and stability of the Earth system (its capacity
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should have a recent doctoral degree in electrical engineering, control engineering, or a related field. For the physics-informed AI position, a doctoral degree in computer science or applied mathematics
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skills, proficiency in quantitative analysis of large datasets and working with pre-trained machine learning models is desirable but not essential To apply online for this vacancy and to view further
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related field; experience with fMRI experimental design and analysis is not required but highly desired; a strong mathematical/programming skill is highly desired; and a track record of research
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance
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will be on the use of novel ellipsometry techniques to characterize pyrocarbon and graphite-based materials. Analysis will be used to support larger irradiation efforts and interpretation of new fuel
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in projects and activities involving commercialization goals across the DOE energy and innovation portfolio by researching strategy, conducting analysis, identifying opportunities to streamline and
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recover from disruptions, as well as the risk of cascading interactions between them. Key responsibilities: analysis of the planetary-scale resilience and stability of the Earth system (its capacity
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science