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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 month ago
and papers supporting the scientific case for polarized deuterons. Contribute to the design, simulation, and performance optimization of the BHCal for the EIC. Participate in the assembly, calibration
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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from telescope data. The design of robust uncertainty quantification tools is a core component of this effort. -On the experiment design side, the group develops simulation and optimization algorithms
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interface between ESM and the latest version of SIESTA, its validation, its benchmarking, and its exploitation in scientific applications. Work will also involve the optimization of SIESTA on the fastest and
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, and method development skills on protein extraction optimization, fractionation, purification, characterization, binding properties, modification, and application in food systems. Also a sloid knowledge
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digital twins to develop innovative solutions for monitoring, analyzing, and optimizing urban systems in real time. The candidate will contribute to modeling interactions between physical and digital
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for the fundamental understanding and engineering design of safe autonomy for autonomous vehicles. The post-doctoral researcher should have a strong background in autonomous vehicles, optimal control, differential
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. Skilled in analytical methods for catalytic/separation agents materials characterization. Solid understanding of chemical kinetics principles. Key Responsibilities: Develop and optimize adsorbent catalytic
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will work with data collected from the field to the spatial scale, and investigate spatial optimization approaches to improve the model parameterization at the spatial scale. We expect that you will be
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will work with data collected from the field to the spatial scale, and investigate spatial optimization approaches to improve the model parameterization at the spatial scale. We expect that you will be