51 optimization-nonlinear-functions Postdoctoral positions at Oak Ridge National Laboratory in United-State
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with world-class scientists, you will enhance your expertise in resource optimization, scalable computing techniques, fault resilience, and advanced AI applications. This role offers unparalleled access
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planning using operation model development, either through development of bespoke simulation/optimization tools, or through application of tools like RiverWare, WEAP, WRAP, StateMod, OASIS, WRIMS, and HEC
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy
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seeking to fill a Postdoctoral Research Associate position to work in the areas of environmental life cycle assessment (LCA), technoeconomic optimization; and industrial energy efficiency and production
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guarantees while minimizing performance impact. Additionally, you will optimize the balance between privacy and utility, addressing the challenges of heterogeneous privacy budgets and varying requirements
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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-building energy performance and systems integration, deployment, and analysis in support of the DOE mission. Staff members lead and participate on teams focused on R&D and deployment of energy-efficient
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for 2D Quantum Magnets) collaboration. While located at the facility, the role is not primarily experimental; instead, the successful candidate will develop forward models, synthetic datasets, and AI tools