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Field
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-time sensing, multi-sensor fusion, and intelligent algorithms can jointly enable safer, greener, and smarter rail operations. Key research topics include eco-driving, environment cooperative perception
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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research on biomedical sensor applications (biomedical sensor interfaces/ integrated mechanical strain sensors) analog/Mixed-Signal Integrated Circuit Design (CMOS, low-power, low-noise design) publication
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interaction and origins of mechanical failure under pressure. They should have expertise in microelectrode arrays and multilevel high-density routing for large-area sensor systems. Experience in multicomponent
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interaction and origins of mechanical failure under pressure. They should have expertise in microelectrode arrays and multilevel high-density routing for large-area sensor systems. Experience in multicomponent
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models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
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planning -Semantic-based Exploration -Source localization -Perception in sensor-degraded environments: -Localization in smoke and dust filled environments -Scene awareness -Biometric/triage evaluations, etc
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models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
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Applications in robotics, autonomous driving, healthcare, smart mobility and sensor networksMore Details and Where to applyhttps://uu.varbi.com/en/what:job/type:job/jobID:824182
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Location: South Kensington About the role: The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple