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Location Lexington, KY Grade Level 12 Salary Range $62,400-111,634/year Type of Position Staff Position Time Status Full-Time Required Education PhD Click here for more information about equivalencies: https
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
strong machine learning and numerical modelling background to add knowledge on the impact of geological heterogeneity and subsurface environments (e.g., depth, exhumation, temperature, pressure) to de-risk
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Movement Sciences at Rutgers University is seeking a highly motivated Post Doctoral Associate to work on translational projects at the intersection of biomechanics, machine learning, exergaming, and mobile
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge
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on Android phones including Kotlin and Java. This position requires skills in software development on Apple iOS phones including Swift. Experience in computational diabetes, signal processing, machine learning
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 14 days ago
establish reference performance and guide the design of more advanced models. The core of the work will then focus on developing and evaluating machine-learning-based NILM methods tailored
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the Job related to staff position within a Research Infrastructure? No Offer Description Job Purpose As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our
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classification for conducting cutting-edge and life-changing research that creates impact in our communities. Additionally, for more than a decade, they have received a national Military Friendly® School
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to the development of novel indoor localization and tracking methods, algorithms, and systems ? Evaluating the performance of such methods, algorithms, and systems via modeling and simulation ? Performance evaluation
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, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating