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Field
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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Posting Title Researcher II/III: EMT Modeling, Simulation and Analysis for Large-Scale Power Systems . Location CO - Golden . Position Type Regular . Hours Per Week 40 . Working at NREL The National
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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stimulated the ever-growing demand for high-energy-density, highly safe, and low-cost energy storage systems. Nevertheless, the continuous large-scale application of lithium-ion batteries (LIBs) is constrained
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. While loading on individual devices is now relatively well understood, existing array-scale investigations have largely assumed steady current conditions and neglected surface waves. Crucially
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, finance or computing science/natural language processing/machine learning. Preference will be given to those who are proficient in Python, adept at processing large-scale data and have worked with large
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for the position. Your tasks This PhD position is part of the ErUM-Data project CmarT, which aims to develop a novel multi-scale imaging approach at synchrotron radiation facilities, particularly PETRA III at DESY
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. Robust reservoir-scale modelling is therefore essential for predicting system performance and informing design and operational decisions. Leveraging geological, hydrological, and thermal models developed
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instruments, large-scale datasets, and an international research community. Training on neutron scattering techniques will be provided. The project is ideal for students who are interested in computational
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Your Job: This work will be part of the project "Role of root mucilage in water and phosphorus uptake in semi-arid environment: Bridging the gap from theories on the pore scale to emergent behaviour