53 phd-position-wireless-sensor-networks Postdoctoral positions at Oak Ridge National Laboratory
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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, industrial engineering
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a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: PhD degree in physics or related discipline completed within the last five years
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE [#27204] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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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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Requisition Id 15435 Overview: We are seeking a Postdoctoral Research Associate, in computational nuclear physics. This position focuses on nuclear theory with an emphasis on fundamental symmetries
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: To be eligible you must have completed a PhD in materials science, chemistry, physics, engineering, or a related field with in the last 5 years. Visa sponsorship is not available for this position
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
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technology by helping to attract, develop, and retain the workforce of actinide scientists to meet the needs of the nation. Topic of Interest This position is supported by the Department of Energy Isotope
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solutions to compelling problems in energy and security. The Environmental Sciences Division of Oak Ridge National Laboratory (ORNL) seeks a creative individual for a postdoctoral research associate position
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. This position focuses on researching, designing, and deploying innovative data pipelines and readiness frameworks to tackle obstacles such as data heterogeneity, scalability bottlenecks, privacy