60 optimization-nonlinear-functions Postdoctoral positions at Oak Ridge National Laboratory
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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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the Manufacturing Science Division (MSD), Energy Science and Technology Directorate (ESTD) at Oak Ridge National Laboratory (ORNL) to work in the areas of life cycle energy impacts analysis, technoeconomic analysis
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position in AI for science. As energy consumption is becoming a serious challenge facing large-scale AI data centers, you will work with experts in this area exploring combination of existing techniques
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species. It can be fine-tuned for downstream applications such as predicting genetic perturbations, optimizing photosynthetic apparatus for performance, selecting top performing genotypes for various
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Section, Nuclear Energy and Fuel Cycle Division at ORNL is seeking candidates to apply for the computational nuclear engineer role. This role is responsible for the development and implementation of methods
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science, and materials engineering, with emphasis on understanding material behavior in complex chemical and radiological environments. Research activities may include the design of functional nanomaterials
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Laboratory (ORNL). As part of our team, you will investigate the atomic and electronic structures in energy and quantum materials and correlate them with relevant properties for energy and data storage
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equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A Ph.D. in material science, or closely related field with
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funded by the U.S. Department of Energy (DOE) Office of Basic Energy Sciences (BES) in the Materials Sciences and Technology Division (MSTD). The successful candidate will be expected to work effectively