954 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dr" "FEUP" uni jobs in France
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- Université de Bordeaux
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optimization under targeted objectives. These approaches will be tested in flows of increasing complexity, including cellular flows, stationary, and fully developed turbulence. Practical information This work
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will connect hospitals across Luxembourg, Germany, and Asia, establishing secure, future-proof communications for sensitive healthcare data. Alongside the network deployment, LIH is developing a real
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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-person and remote meetings Coordinate scientific report processes, including spreadsheet configuration, information sharing, and online resource management Help coordinate author submissions, including
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localization, or escape responses, thereby revealing generic control principles Practical information This work will be jointly supervised by Christophe Eloy (IRPHE, Centrale Méditerranée), whose research spans
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waveforms targeting spectrum bands above 140 GHz. These frequency ranges are envisioned for very high-data-rate point-to-point links (x-haul) and/or joint communication and sensing applications. In both cases
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bioinformatic facility, data storage and analysis facility and animal housing of which bees, drosophila, zebrafish, xenopus and mice facilities. Deep sequencing and proteomics facilities can be accessed in nearby
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, spatial relationships, and contextual information. The framework would focus on real-time inference and investigate temporal search strategies using vision–language large models to identify anomalies in
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unit (UMR 7248) from UCA and CNRS. Abstract Optical flow estimation is a key task in computer vision, particularly critical for autonomous navigation where accurate motion perception is essential. It can
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in the training data and can naturally arrive during deployment (i.e., a distribution shift), increasing the risk of obtaining wrong predictions. Consequently, OoD samples detection is crucial in