36 electrical-machine-"https:"-"https:"-"https:"-"https:" PhD positions at Monash University
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-dimensional materials. I am interested in creating a low-power topological transistor, in which an electric field can switch a material from a conventional insulator (“off”) to a topological insulator (“on
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors
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the optical-to-radio wavelength range, from major surveys and space telescopes (e.g: Gaia, SDSS, JWST, Hubble, Roman, Rubin-LSST). These are analysed using advanced machine learning and data-driven methods. My
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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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(or equivalent value) Research Living Allowance, at current value of $41,555AUD per annum 2026 full-time rate (tax-free stipend), indexed as per current ARC rates found here: www.arc.gov.au/salaries-and-stipends
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will receive a Research Living Allowance, at current value of $42,606 AUD per annum 2026 full-time rate (tax-free stipend), indexed plus allowances as per RTP stipend scholarship conditions at
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spectroscopy and Gaia data of star clusters to decipher the mystery of the Lithium-rich giant stars" (with Prof John Lattanzio) "The origin of the heavy elements: Computer simulations of neutron-capture
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-bandwidth continuous magnetic sensing of an ensemble of electric spins Developing a spatially sensitive optical magnetometer catheter probe Ultra-sensitive zero-field using quantum anti-crossings web page
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, remote until then) Contract Type: Full-time Duration: 4-year fixed-term appointment Remuneration: Upon enrolment the successful applicant will receive a Research Living Allowance, at current value
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with