21 phd-data-analytics Postdoctoral positions at Oak Ridge National Laboratory in united-state
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. Excellent analytical skills with high attention to detail. Experience with geospatial data visualization tools such as ArcGIS, Cesium, or similar. Familiarity with urban-scale building energy modeling
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data and develop data-driven methods for predicting land loss and ecosystem transitions in wetland-rich landscapes of the Gulf Coast with a focus on coastal Louisiana. This candidate will directly
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characterization using advanced analytical tools, such as synchrotron, neutron, and laboratory equipment. Experience in data analytics and modeling tools. Special Requirements: Applicants cannot have received
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, Chemistry (i.e., Analytical, Physical), or a related field completed within the last 5 years. Demonstrated skills and hands-on experiences with advanced materials characterization equipment and/or data
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guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Independently and collaboratively lead field
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evolution, phase stress, and defect evolution using advanced data analysis tools. Perform alloy fabrication and processing (e.g., arc melting, heat treatment) and relate processing to performance. Apply
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and data analytics with applications in a large variety of science domains. NCCS is home to some of the fastest supercomputers and storage systems in the world. This position is in the Technology
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, industrial engineering, electrical engineering, environmental
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preparation of well-characterized target compounds. Apply a range of analytical and characterization techniques to evaluate chelator structure, affinity, selectivity, and metal complexation, including radio-TLC
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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal