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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 1 month ago
intelligent decisions. In particular, when low latency is critical, these models are deployed directly on devices at the edge, close to the equipment itself — a necessary condition for smarter, faster
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improve prognostication in critically ill patients by integrating cardiovascular risk factors into existing ICU prediction models. by combining cutting-edge data science with advanced clinical expertise and
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a given discrete-event model that is safe with respect to certain requirements. Yet, the application of the approach is limited by the fact that tools for synthesis cannot always deal with the state
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are offering a fully funded PhD position in the field of Physics-informed Learning-based Control. This interdisciplinary research area bridges control theory, machine learning, and physics-based modeling
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for simulating flow and pressure response, as well as thermal storage. Also analyzing new and existing data and calibrating models. Duties of the position Complete the doctoral education until obtaining a
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background in immunology, flow cytometry, as well as experience in rodent models. The selected candidate will be expected to work independently while interacting in a collaborative environment. Education
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of system modelling and control, with emphasis on applications in power electronics. The student will be employed as researcher in Prof. George Weiss' control and power electronics team for a period of 15
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Predoctoral researcher at the Targeted Therapeutics & Nanodevices Research Group (Project BRAINZYME)
physiological conditions. • Examine their performance in 2D and 3D systems, including chemical and genetic disease models, multi-cultures and organ-on-a-chip devices. • Investigate the biodistribution as
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focus of this position is research on hydrodynamic loads and performance of tidal turbines under realistic flow and clearance conditions. Tidal-stream energy offers a predictable, complementary renewable
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excursions. The PhD student will work on analysing the reliability of paleomagnetic records, deriving uncertainty estimates, and building time-dependent models of the paleomagnetic field evolution using a