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-year full-time fellowships will be awarded to outstanding candidates who develop innovative, collaborative projects between a Crick-based research group and a Partner University-based research group
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for Trustworthy AI , which is a research centre dedicated to developing trustworthy AI systems that are accurate, fair, transparent, safe, and well-governed. The centre brings together researchers from
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the research component of the project. Write and review research papers, present research outcomes, conceptualise new ideas and develop plans for independent research which could have a considerable influence
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 17 hours ago
in the Office of Translational Sciences at the FDA is recognized for excellence in the application and communication of statistical science in drug regulation and development. We play a central role in
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effectively with researchers, clinicians, data custodians, health services and other partners. Key responsibilities will include: Research: Lead the quantitative components of the project, including developing
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-leading health research institute at UNSW Sydney. We work to eliminate infectious diseases, globally. Our specialisation is in developing health solutions for the most at-risk communities. Putting
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change would be highly desirable. The Research Fellow will develop ambitious, frontier research questions and lead high-quality peer-reviewed publications. They will contribute to regional field campaigns
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to make an important contribution to the development of the Department of Engineering's research while allowing you excellent opportunities to develop your career in an exciting and progressive institution
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or machine learning with a particular focus on analysing large-scale behavioural data? Are you interested in developing new methodological approaches capable of extracting behavioural insight from increasingly
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The Research Fellow (RF) will conduct research in the field of coastal dynamics, with a focus on the development and application of machine-learning-enhanced coastal models. The project aims