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research into real-world products. The candidate will spend part of their candidature embedded within Aervision's research and development team, gaining hands-on experience in commercial AI development
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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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the School of Social Sciences. The project will focus on the lived experience and impacts of displacement linked to a community impacted by climate-related disaster. Using primarily qualitative methods
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is carried out within the LHCb collaboration that runs one of the four large experiments at the Large Hadron Collider at CERN as well as towards future collider developments. I supervise a number of
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experience, deemed equivalent by the Graduate Research Committee (or delegate); and Preferably, be available to commence as a full-time PhD candidate in Q4 2026 (or by arrangement). Demonstrate strong interest
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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are possible (in coordination with Prof Ulrik Egede) or with other Monash supervisors on a case-by-case basis. "Confronting Theory and Experiment at the Large Hadron Collider" "Simulations of Quark and Gluon
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do this by compiling and analysing a database of past funding decisions. PACE will also (WP-2) build an evidence-base of real patient experiences and preferences to guide decisions, ensuring health
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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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Peterson). This project combines both theory and experiment. "Geometric-flow across diffraction patterns in 4D scanning transmission electron microscopy” (with Assoc Prof Scott Findlay and Dr Timothy