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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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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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technologies including thermal energy storage (TES) and hydrogen technologies integrating with concentrating solar power (CSP). Responsibilities under this position include leading research work on modeling
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collaborative efforts among researchers at the University of Utah and UC San Diego in developing and applying methods in predictive and causal modeling of complex biomedical and social processes and systems
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, metabolomics, clinical samples, and animal models to accomplish these goals. The results will lead to the development of novel therapeutic strategies for the clinically relevant molecular subsets of lung cancer
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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Computing (e.g., memristor modeling/simulation/manufacturing) and Edge AI related areas (e.g., AI algorithms, AI accelerator, VLSI). Background Investigation Statement: Prior to hiring, the final candidate(s
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postdoctoral fellow interested in gaining training and experience in disease modelling and transnational science. The successful candidate will lead collaborative efforts among basic and clinical researchers and
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. Recruits and screens participants, reviews records and surveys, and maintains databases. Performs routine data analysis and prepares reports, data visualizations, and models. Essential Functions
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for clinical use. Generative and Predictive AI for Clinical Decision Support and Statistical Inference Develop biologically informed statistical methods and uncertainty estimation models to train deep learning