38 computer-vision positions at National Renewable Energy Laboratory NREL in United States
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. Computational modeling of hydrogen production and storage. Write technical reports and contribute to project review meetings. Publish research results in peer-reviewed conferences and high-impact scientific
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. Interest in exploring robot programming, computer vision sensing, programming, and prototyping. Publication record and ability to lead innovative research projects. Excellent communication skills and a
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Posting Title Group Manager - Chemistry Materials Computational Sciences Directorate, Chemistry and Nanoscience and Materials Science Center . Location CO - Golden . Position Type Regular . Hours
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Posting Title Business Support Professional II- Materials, Chemical and Computational Sciences . Location CO - Golden . Position Type Regular . Hours Per Week 40 . Working at NREL The National
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various computer software programs. * Must meet educational requirements prior to employment start date. Additional Required Qualifications Demonstrated experience securing distributed critical
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believe they have the skills and ambition to succeed at NREL to apply for this role. Job Description The High Performance Algorithms and Complex Fluids (HPACF) Group in the NREL Computational Science Center
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of a 3.0 cumulative grade point average. Undergraduate: Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution. Post Undergraduate: Earned a bachelor’s
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believe they have the skills and ambition to succeed at NREL to apply for this role. Job Description The NREL Materials, Chemical and Computational Science (MCCS) directorate has an immediate opening for a
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graduate within the last three years. * Must meet educational requirements prior to employment start date. Additional Required Qualifications PhD in Computer Science, Computer Engineering, Computational
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computing, and advanced choice modeling techniques are encouraged to apply. Relevant areas of research the candidate will be working on include: (i) Travel demand modeling, (ii) Econometric and statistical