22 chromosome-structure-imaging Postdoctoral positions at Oak Ridge National Laboratory
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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, pyrometry, spectroscopy, co-axial and off-axis high speed imaging, and more) for process monitoring and diagnostics. Develop and implement data acquisition, signal processing, and data analytics frameworks
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, fusion, materials, life sciences, and other strategic domains. Investigate novel approaches for balancing efficient I/O, interoperability, and scientific validity in AI-ready datasets. Design, prototype
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-based systems Background in topology optimization and structural design Experience in thermomechanical characterization of polymer materials Demonstrated experimental capabilities and a strong
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characterizations. Experience with user facilities. Data analysis of structural, electronic, magnetic, and topological properties. Work with others to maintain a high level of scientific productivity. Publish
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structure and statistical mechanics codes, and data science tools would be highly desirable Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and
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of NTI and CNMS to develop HPC workflows that can perform multi-fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques Develop physics-informed
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properties of the above materials. Collaborate with ORNL postdocs and staff who are involved in structural characterization. Participate in the development of new ideas and projects. Present and report
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy