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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
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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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. We also collaborate closely with researchers at CERN and at Fermilab, and with our LHCb colleagues through the Monash-Warwick Alliance in Particle Physics. Joint projects with an LHCb focus
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Collider at CERN in Geneva. During the past few years vast datasets have been collected, allowing us to probe the Standard Model with world-leading sensitives. Research projects typically involve analysing
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motivation Testing whether the system performs equitably across cultural and language groups Training spans intervention design, trial methodology, human-computer interaction, and applied machine learning
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protoplanetary discs* Modelling planetesimal collisions during the planet formation process* Example projects in black hole accretion include: Observational appearance of tidal disruption event flares across
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. Selection criteria: Prospective applicants must satisfy Monash University PhD entry requirements as outlined in the Monash Graduate Research Admission Procedure, including: Open to domestic candidates
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skills, to undertake a PhD in the Faculty of Arts are available at https://arts.monash.edu/graduate-research/application-process/ . Applicants should ensure they familiarise themselves with
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requirements, including English-language proficiency skills, to undertake a PhD in the Faculty of Arts are available at https://arts.monash.edu/graduate-research/application-process/ . Applicants should ensure
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plasma‑filled bubbles are injected into water to create nitrogen oxides (NOₓ) directly from the air. Because the bubbles are so small, they provide a large contact area, making the process highly efficient