59 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dr" "UCL" PhD positions at Monash University in Australia
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to work closely with other leading academics at Monash University, including Professor Carol Propper , A/Prof Terrence Cheng and Dr Danusha Jayawardana . As a candidate in the CHE Integrated PhD Program
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St Kilda Rd, Melbourne in August 2026 Employment Type: Full-time Duration: 3-year fixed-term appointment Supervisory Team: Dr Samantha Chakraborty, Professor Tari Turner Remuneration: The successful
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Director, Opportunity Tech Lab; Chair of the Steering Committee, Monash Business Behavioural Laboratory) Dr Mor Vered (Department of Data Science & AI, Faculty of IT) Dr Estelle Wallingford (Department
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will have the opportunity to interact with gravitational-wave researchers throughout Australia and around the world. Students in my group use data from the Laser Interferometer Gravitational-wave
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Understanding factors related to student retention and experience in physics and astrophysics major units. Using quantitative (surveys) and qualitative data (interviews with students) this project
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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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, 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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are supported by quantum mechanical theoretical formalisms. Our fundamental findings yield promise for future applications in electronics, optoelectronics, spintronics, information processing and storage, sensing
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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