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Field
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materials chemistry and surface science, with a focus on the development, operation, and analysis of specialized physical characterization techniques. Position Status Full Time Posting Number 26FA0156 Posting
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20 Feb 2026 Job Information Organisation/Company Université de Pau et des Pays de l'Adour Department SIAME Research Field Engineering » Computer engineering Physics » Computational physics
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conduct in vivo experiments to investigate retinal neurovascular dynamics using the AO-RSO system; Develop or adapt high-speed image acquisition and processing methods for simultaneous measurement
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 24 days ago
between the device-dependent and the device-independent scenario, and (iv) developing its applications in communication, computation, and security tasks Where to apply Website https://jobs.inria.fr/public
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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potential. Given the medicinal chemistry–driven nature of this project, we are specifically seeking a candidate with documented experience in lead optimization, including structure–activity relationship (SAR
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receipt. Information on the competition procedure can be found in the Guide for Applicants LINK PL: https://pwr.edu.pl/uczelnia/europejska-strategia-dla-naukowcow/otm-r LINK EN: https://pwr.edu.pl/en
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testing of model-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs
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extensive network of experienced researchers and personnel. Successful applicants will be involved in the operation of the world’s most sensitive interferometers, and/or the R&D program for future detector
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities