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RAP opportunity at National Institute of Standards and Technology NIST Development of New Computational Methodologies for Molecular Simulation of Soft Materials Location Material Measurement
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development of wide bandgap device-based integrated motor drives, advanced control techniques for next-generation drives, and innovative approaches to reducing passive components in electric drive systems. Your
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areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research
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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic
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in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses on methodological development in cryo
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analytical many-body calculations Expertise in one or more of the following: computational methods, software and algorithm development, high-performance computing, and data analysis Proficiency in relevant
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective
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/adaptive algorithms, offline and online data analysis, conducting experimental research, and online evaluation of the developed adaptive strategies with a robotic application. The prospective students can
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transmission of information and energy, systems theory, and computational hardware and software. ECE students are encouraged to develop synergies with disciplines outside of engineering. The candidate should
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between