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Field
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, ill-posed nonlinear inverse problems Numerical optimization techniques Machine learning Strong programming skills in Matlab and/or Python are required. These should be documented, for example through a
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distribution systems, EV charging modeling, distributed energy resources, optimization, control, machine learning, hardware-in-the-loop simulation. Expertise in programming languages such as Python, C
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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one of the following areas is required: Numerical methods for large-scale, ill-posed nonlinear inverse problems Numerical optimization techniques Machine learning Strong programming skills in Matlab and
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technical background in at least one of the following areas: Mathematical optimization, Resource allocation, Machine learning, Wireless communication. Good communication skills in oral and written English
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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research across NIH and the federal government to foster knowledge and optimize health across the lifespan. What will I be doing? As a fellow, you will collaborate with NIH scientists on an evolving area of
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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Stimulated Raman Spectroscopy (PARS) method. Research tasks will include design, development and optimization of the full PARS system including a Raman cell pumping scheme, a photoacoustic cavity, and an
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are expected to be the CO2 capture technology for early large-scale deployment. A detailed understanding of the CO2 capture solvent is therefore necessary to ensure development and application of an optimal