403 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" positions at Oak Ridge National Laboratory
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with data-driven modeling, including emerging approaches involving foundation models or scientific LLMs Special Requirements: This position requires the ability to obtain and maintain a clearance from
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) to work on the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA). The successful candidate will contribute to the development and evaluation of the Energy Exascale Earth System Model's land
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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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Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving
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Requisition Id 17180 Overview: We are seeking a Computational Physicist to carry out modeling of plasma transport and plasma-material interactions (PMI) in the Materials Plasma Exposure eXperiment
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) to work on the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA). The successful candidate will contribute to the development and evaluation of the Energy Exascale Earth System Model's land
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community electrification and decarbonization. Understanding for creating, modifying, and analyzing OpenStudio and EnergyPlus building energy models. Strong software development skills for automation of many
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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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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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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