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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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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
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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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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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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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spectroscopy" "Chemical abundances in star clusters using Korg, the first spectral synthesis code developed in two decades" web page For further details or alternative project arrangements, please contact
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. Analysis of this data will employ both qualitative and quantitative methods. Work on WP-2 will suit someone with an interest and aptitude for coding administrative data using large language models. It will