174 computational-physics "https:" "https:" "https:" "https:" "Brookhaven National Laboratory" positions in Luxembourg
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, Sustainable Materials for Next Generation of Pneumatic Tires, Structure-Process-Properties Relationships. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute
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research results in leading international conferences and journals in machine learning, computational biology, and AI for science The postdoc will work at the interface of machine learning, genomics, and
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15 Apr 2026 - 23:59 (Europe/Luxembourg) Country Luxembourg Type of Contract Temporary Job Status Full-time Hours Per Week 40 Is the job funded through the EU Research Framework Programme? Not funded by
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the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Post-Doc
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Are you passionate about cutting-edge research in Computer Vision and Artificial Intelligence? Our research group CVI2 - Computer Vision, Imaging and Machine Intelligence, headed by Prof. Djamila
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We seek a highly motivated AI scientist, biostatistician or computational biologist who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific
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Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Fixed term contract | Belvaux | 30 Months Are you passionate about
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Fixed term contract | Belval |24 Months Are you
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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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The University of Luxembourg invites applications for a fully funded doctoral position in mathematical and computational modelling within the framework of the doctoral training unit Forest Function