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Explainable AI on genomics data The candidate will: - Start by familiarizing with existing research and methods for genome interpretation - Familiarize with the sequencing data and its pre-processing - Study
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 2 months ago
of publications in quantum information theory. Familiarity with Bell nonlocality, operator algebras, SDP relaxations of polynomial optimization problems, quantum correlation protocols, experimental physics and / or
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an agile co-design and co-development process across NumPEx and with the CSE application community. The Exa-DI and the Exa-SofT projects are looking for a highly motivated candidate with a strong background
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an assessment framework that will enable us to better identify and propose, in a transparent manner, management measures for optimal sustainability, tailored to each study area and applicable to different sites
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Louis Lions. - Design and implement innovative methods for the numerical solution of wave propagation problems within the FreeFEM software, using high-performance computing - Optimize the code
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benchmarking and comparative evaluation of gene perturbation models across diverse single-cell datasetsCollaborate closely with Helical-AI on scaling, optimization, and integration of the developed models within
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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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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Postdoctoral researcher (M/F). Modeling damage during earthquakes. Comparison with geophysical data.
work will consist of optimizing the codes that generate input data and process output data. • The postdoctoral researcher will carry out comparative work using data from geodesy and seismology
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signal-to noise Post-processing: denoising, reconstruction algorithms Comparison with high-field MRI: deep-learning and other AI modalities for low-field MRI optimization Close cooperation with