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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid
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of the proteins to advance the photophysical research on (photoswitching) proteins developed in the laboratory. The lab The Stiel Lab is one of the laboratories at the Institute for Biological and Medical Imaging
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and with the 2AT team at Institut Pprime to develop resolvent-based modelling tools for turbulent jets in cases where the jet mean flow is three-dimensional. The researcher shall: - Develop a high-order
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and with the 2AT team at Institut Pprime to develop a shape-optimisation tool based on resolvent analysis, applied to landing-gear aeroacoustics The researcher will develop a numerical methodology based
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of Institut Pprime the CNRS is recruiting a Researcher within the framework of the SALSA project (funded by the DGAC and coordinated by Airbus). The successful candidate will contribute to the development and
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and with the 2AT team at Institut Pprime to develop an innovative jet-noise prediction tool. The researcher will develop a novel jet-noise prediction tool based on a resolvent analysis of the Navier
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legal research methods. You possess demonstrable didactic skills. Preferably, you already have experience in developing research proposals. You are motivated by autonomy and responsibility and strive
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the partners and within the team and translating the partners¿ requirements into the research programme at Liverpool. Contributing to work developing state-of-the-art energy system modelling methods. Being
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colleagues within the Division and throughout the University as appropriate to develop mutual research and training interests. This position will work on developing innovative therapies and rehabilitation
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on student advisory committees as needed), research 90% (includes performing literature reviews; developing studies; experimental design; performing experiments; collecting, analyzing, and interpreting data