36 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" PhD scholarships at Monash University in Australia
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health-related field Experience with mixed methods research, systematic reviews & data analysis Strong communication and organisational skills Australian citizenship or permanent residency (If applicable
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(or equivalent) in a health-related field Experience with mixed methods research, systematic reviews & data analysis Strong communication and organisational skills Australian citizenship or permanent
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candidates holding (or eligible to obtain) a valid student visa. Candidates with strong academic and research track records are particularly encouraged to apply. For further information, please follow the link
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control
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candidates holding (or eligible to obtain) a valid student visa. Candidates with strong academic and research track records are particularly encouraged to apply. For further information, please follow the link
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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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, which are some of the most numerous stars in the Universe. "Weighing stars using stellar vibrations: Asteroseismic masses of Red Giant Stars using space telescope data" "Using optical telescope
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, to trace the chemical enrichment of the universe, and even to better understand planet formation. Most of my research involves huge data sets with observations of all different kinds (e.g., photometry
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electromagnetic signatures, primarily focussed on linking the data from these exciting experiments with our theoretical understanding of gravity and the most extreme regions of the Universe. I am a member of the
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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