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Field
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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel
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saved and continued before the closing date. If saving to continue later, be sure to follow the instructions provided and bookmark the URL. An email confirmation will be provided when an application is
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financial security with 17% superannuation Supportive flexible work arrangements underpinned by our commitment to inclusion and well-being Career development opportunities, including access to a range of
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) Offerings/ Benefits/ Culture: The University offers excellent conditions and benefits which can be further explored on our “Work at UC ” site such as flexible, family-friendly policies, on site gym, on site
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, please apply. We are looking for diversity in experience, thought and approach. This role is part of a progressive and supportive team. We support flexible work arrangements, remote working/working from
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 25 days ago
activities will depend on the successful candidate’s expertise and career interests. The starting date for this one-to-two-year fellowship is flexible but no sooner than July 1, 2027. Stipend is commensurate
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for SHM and post processing techniques e.g. computer vision Experience in NLFEA of concrete structures Impact loading To be successful at shortlisting stage, please ensure you clearly evidence in your
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development opportunities. Comprehensive occupational healthcare, wellbeing services, and access to CampusSport services at staff rates. Flexible working hours, remote working opportunities in accordance with
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English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials