61 cloud-computing-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs at Oak Ridge National Laboratory
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). Knowledge of high-performance computing or cloud environments for large-scale data. Strong collaboration skills and ability to work in interdisciplinary teams. Special Requirements: Applicants cannot have
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Director's office can be found here: https://www.ornl.gov/content/research-integrity . Basic Qualifications: A PhD in physics, chemistry, biochemistry or a related field completed within the last five years
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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of the ORNL scientific community, you will be expected to commit to ORNL's Research Code of Conduct. Our full code of conduct and a statement by the Lab Director's office can be found here: https
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, Cloud, and HPC continuum. This position centers on advancing intelligent workflows that enable seamless processes in autonomous discovery, complex data integration, workflow provenance, and interactive
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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Requisition Id 15434 Overview: The Advanced Computing in Health Sciences (ACH) section at the Oak Ridge National Laboratory (ORNL) is seeking qualified applicants for a Postdoctoral Research
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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Ph.D degree in electrical engineering, computer engineering, computer science, or a related discipline Demonstrated experience developing, training, and applying AI algorithms to physical sensor data
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purification of electrode or electrolyte systems and evaluation of their battery performance. The program will have a strong collaborative component with characterization (e.g. electron microscopy