19 linked-data "https:" "https:" "https:" "https:" "EMBL" Postdoctoral research jobs at National Renewable Energy Laboratory NREL
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team. The initial appointment is for one year, with possible renewal up to three years. We encourage interested candidates to see the following papers: https://pubs.rsc.org/en/content/articlehtml
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to guide experiments. For some sample publications by the group please visit the below links albeit please note this is more of an experimentalist position compared to characterization. https
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realistic biofuels. https://cbi.ornl.gov/ The successful candidate will work closely with the CBI Lignin Valorization team to conduct biorefinery analyses related to lignin valorization to fuels and
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group performs research to advance wave energy, tidal energy, ocean current energy, riverine energy, and ocean thermal gradient energy technologies (i.e., marine energy technologies) - see https
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instrument operation and maintenance experience (e.g. GC/MS and LC/MS analysis), including hands-on operation, and including data reduction and interpretation and communication of results Demonstrated
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building closed-loop experimental systems that integrate advanced scientific instrumentation, AI-driven decision-making, data infrastructure, and workflow orchestration. The platform will accelerate the
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to conduct research at the intersection of machine learning, computer vision, large-scale data analytics, and photovoltaic (PV) system performance . The successful candidate will join a
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meetings and seminars, and assist in engineering and writing statistics and deep learning software, statistical and probabilistic data analysis of results, algorithm design, and publication
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characterization data from experiments; and work with project task leads to write papers and reports. . Basic Qualifications Must be a recent PhD graduate within the last three years. * Must meet
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characterization assignments, including sample preparation, data collection, and preparation of concise technical reports on short turnaround. Explore integration of foundation models and large language