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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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, advanced imaging, AI/machine-learning approaches, mathematical modelling, or novel computational methods are especially welcome. The successful candidate will receive a competitive start-up package and join
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available1Company/InstituteDepartment of Computer ScienceCountryNorwayCityTrondheimPostal Code7491StreetHøgskoleringen 1Geofield Contact City Trondheim Website http://www.ntnu.no Street Høgskoleringen 1 Postal Code
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SINTEF, Equinor, and Total. The main objectives of the project include the development and the integration of signal processing and machine learning methodologies aiming to improve flow assurance via field
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
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Norwegian University of Life Sciences (NMBU), within the Faculty of Science and Technology(REALTEK), invites applications for a PhD position in Applied Causal Machine Learning. The position is affiliated with
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and research interests, the project may focus on one or more topics such as cyber risk assessment, human–machine interaction, cyber risk communication, cybersecurity training, or organizational and
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engineering disciplines, including structural mechanics, hydrodynamics and machine learning Strong programming skills in Python and/or MATLAB Experience with scientific computing, CFD/FEM software, potential
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under
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and Electric Machines research group (PEM), which is one of four research groups in the department. The main responsibility of the Professor/Associate Professor will be research and teaching within