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/scholarships/scholarship-policy-and-procedures The Opportunity This PhD scholarship forms part of a collaborative research program on digital transformation, data practices, and everyday life in Indonesia
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projects that involve data analysis, the application of artificial intelligence, the development of new detection techniques, and the exploration of new experimental methods through collaboration with our
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challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up
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known as Team COMPAS -- includes a number of amazing undergraduate and graduate students, postdocs, alumni, and other fantastic collaborators. Please contact me if you are interested in joining our group
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combination of multi-wavelength observational data with sophisticated simulations. I am a member of various collaborations, including Australia's OzGrav Centre of Excellence for Gravitational-wave Discovery
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and families. The successful candidate will join a highly collaborative research team using linked employer–employee administrative data, including healthcare use data, and state-of-the-art econometric
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, or High Second-Class Masters degree or equivalent in the health-related field. Strong quantitative research skills. An understanding of quantitative research methodology and data analysis. Evidence of
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health economics, labour economics, economics and econometrics. We will also consider other quantitative disciplines such as data science, mathematical statistics, actuarial science or public health
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measurements in particle physics. Many of my projects are informed directly by current measurements, e.g. addressing new or unexpected features seen in the data. Others focus on improving the formal accuracy
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, computer science, software/systems engineering, and data science. Experience with NLP/LLMs and/or requirements engineering is highly desirable; familiarity with model-based systems engineering, safety-critical