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agnostic, multi-sector machine learning tools to efficiently handle datasets generated by integral field spectroscopy and time domain surveys. The testbed for the machine learning tools is the question
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Star Formation. CARINA is focussed on developing case agnostic, multi-sector machine learning tools to efficiently handle datasets generated by integral field spectroscopy and time domain surveys
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simulation tools for the analysis, design, and validation of aerospace systems, including realistic operational scenarios and human–machine interaction. Where to apply Website https://careers.polito.it
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the interface of machine learning and biology (tools developed by the team: https://github.com/cantinilab). The team is composed of 8 people : 3PhD students, 3 post doc, 1 research engineer and 1 assistant
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fields : Computer Vision, Machine Learning, Pattern Recognition • Strong programming skills in Python • Good knowledge of Linux tools and environment • Autonomous and rigorous • Curiosity and creative
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Forefront Astronomical Instrumentation to Probe Intermediate Mass Star Formation. CARINA is focussed on developing case agnostic, multi-sector machine learning tools to efficiently handle datasets generated
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records, aiming to co-create practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in machine learning, neuroscience
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accessibility-related issues Understanding of Universal Design for Learning and instructional design Knowledge of digital tools for teaching and learning Experience with Canvas LMS, YuJa Panorama, ScreenPal
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electrochemical regeneration. This PhD project will develop next-generation computational tools to accelerate the discovery of such materials. The successful candidate will work at the interface of chemical
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the EnRDIA project, backed by the ANR DIA-SOLAIRE projecthttps://dia-solaire.univangers.fr/fr/index.html ), we are recruiting a design or research engineer to develop an innovative digital tool based