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: Develop physics-based, data-driven, and hybrid (physics-informed ML) models of thermal systems to capture dynamic thermal behavior Validate models against experimental data and refine model accuracy and
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data and develop data-driven methods for predicting land loss and ecosystem transitions in wetland-rich landscapes of the Gulf Coast with a focus on coastal Louisiana. This candidate will directly
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understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred Qualifications: Experience in implementing ab initio simulation codes such as VASP
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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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sponsors, isotope user communities, or other federal sponsors is preferred. Experience developing and implementing automation, process controls, modeling and simulation, process and equipment configuration
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/Responsibilities: Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions. Design and implement
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, the successful candidate will focus on research and development of grid-edge sensing, reliable timing, model aggregation, and hardware-in-the-loop validation for modern power systems. The objective is to improve
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objectives. Major Duties/Responsibilities: Develop and automate image processing and data science workflows that support mission-driven analysis in classified environments. Design, implement, and maintain
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Computer-Aided Design (CAD) software, creates robust, detailed 3D models and drawings that represent solutions to complicated manufacturing, assembly, and customer requirement driven equipment
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teams of scientists, engineers, and technologists from across the DOE complex and academia. Grow your career in a mission-driven, innovation-focused environment with access to professional development and