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Research Fellow, Data Scientist (Wearable Technologies, Digital Health & Artificial Intelligence) Job No.: 697562 Location: Clayton campus Employment Type: Full-time Duration: 3-years fixed-term
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two-year fixed term Postdoctoral Fellow in Neuro-Symbolic Artificial Intelligence. This Level A position is expected to conduct research in Neuro-Symbolic AI for reasoning and decision-making
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
considered. Design new algorithms and systems at the cutting edge of artificial intelligence research Collaborate with a vibrant and internationally recognised research group at ANU Work with industry and
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intelligence can support the development and use of computer algebra systems, including mathematical programming, software verification and testing, documentation, and new ways for researchers to interact with
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together through cutting-edge research in digital media forensics, multimodal data correlation, artificial intelligence, and cyber-enabled trust technologies. Working alongside world-class researchers and
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Opportunity Join a visionary and collaborative research team driving world-leading advances in melanoma and skin cancer research. Working at the intersection of data science, artificial intelligence, medical
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excellence, collaboration and positive societal impact experience in health literacy, digital health, social media research, artificial intelligence, health communication, misinformation or implementation
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questions relevant to agriculture or similar scientific fields (e.g. ecology, geology, genomics). Theoretical understanding of the application of modelling, simulation or artificial intelligence
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them The Opportunity Join the Department of Data Science and Artificial Intelligence as a Research Fellow and play a leading role in advancing artificial intelligence research for healthcare
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, as well as traffic safety and sustainable transport systems. Expertise in the development and application of advanced artificial intelligence techniques for autonomous driving. Demonstrated track