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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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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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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
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for the development of digital forensic tools for prevention, detection and prioritization in Spain (ONSEXGROOM:ES) https://www.upv.es/entidades/SRH/conypi/A1272732.pdf Where to apply Website https://sede.upv.es
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equipment and tools. Ability to follow written and oral instructions, and build understanding of procedures for working at an historic site. Required Experience: Experience using power and hand landscape
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | about 13 hours ago
was installed at IT4Innovations in 2025. For more details, see www.it4i.eu . Activity description: · modelling and optimization of electrical networks using open source tools (preferably Julia or Python