15 coding-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" "DIFFER" PhD positions at Monash University
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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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engineering. Experience with experimental measurements, image-based diagnostics, laboratory testing or quantitative data analysis. Coding and analysis capability in Python and/or Julia, preferably with
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University Supervisor: Dr Mega Kar Graduate Research Degree: Doctor of Philosophy (course code: 3291) Application type: Candidature only Enquiries: Dr Mega Kar, [email protected] Applications close: Monday
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simulations to better understand the evolution of these discs and synthetic observations to compare to real observations. Possible projects include: "The evolution of dust in warped discs with different
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group’s projects often combine astronomy, coding, visualisation, and data science, and work well for students who enjoy either astrophysics, machine learning/coding, or both. The multidimensional structure
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LHCb experiment Searching for matter-antimatter differences in charm hadron decays Developing new probes to characterise proton-proton collisions web page For further details or alternative project
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supergiant stars right before the explosion Searching different astrophysical channels that produce r-process elements Connecting the properties of long-duration gamma-ray bursts and associated supernovae web
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nucleosynthesis in violent episodes suffered by ancient stars" (with Dr Carolyn Doherty) "Applying 3D stellar hydrodynamics findings to 1D stellar codes: Improving the modelling of convection in stars" web page
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to uncover the role of structure in the glass transition and how the disordered structure of a glass gives rise to unique glass behaviour such as ageing and brittle mechanical failure. Unlike crystals which
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datasets with different configurations (e.g., number of channels, sampling frequency and resolution). To leverage large-scale self-supervised learning to train models on unlabeled EEG data, reducing reliance