23 combustion-modelling-postdoc Postdoctoral positions at Oak Ridge National Laboratory
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characterizations, and models that are suitable for nuclear fuel performance codes. Knowledge and experience of x-ray computed tomography, mechanical testing, and finite element modelling is desirable. Major Duties
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Substantial programming skills using Python or modern C/C++ Experience with machine learning and deep learning libraries Experience building AI models in platforms such as TensorFlow, Keras, or PyTorch
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maturation, characterizing performance and properties of nuclear fuels and materials, and generate the data to advance physical modeling and simulation. The primary function of this open position is to perform
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, analyzing data from complementary techniques such as scanning microwave impedance microscopy, Kelvin probe force microscopy, and cathodoluminescence, as well as collaborating with theorists for data-model
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techniques (such as, large language models) in the neutron powder diffraction data life cycle. This work will be conducted collaboratively with other scientists within the Neutron Scattering Division and
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. The goal of this work is to investigate the dynamics of beams with intense space charge and benchmark simulation models against experimental results. As a U.S. Department of Energy (DOE) Office of Science
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research analysis on geothermal well development and other advanced energy technologies that could achieve transformative gains in energy efficiency. Ability to develop optimization and life cycle models
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to accomplish multiple tasks within deadlines, and adapt to ever changing need Special Requirements: Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and
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computing AI on High-Performance Computing (HPC) cluster. Examples on areas of research interest include but are not limited to: Vision transformers. AI foundation models. Computing and energy-efficient
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, transportation, and computing. The CIR Group is a part of the Geospatial Science and Human Security Division at ORNL. The CIR group is heavily engaged in modeling risk and resilience of critical infrastructures