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will focus on developing and applying novel machine learning and AI methods to improve the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics
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biomarkers (CA125, HE4, ROMA score). Feature selection algorithms (e.g., Boruta) and statistical imputation methods (e.g., KNN/Iterative Imputer) are applied to optimize variable efficiency and handle
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biomarkers (CA125, HE4, ROMA score). Feature selection algorithms (e.g., Boruta) and statistical imputation methods (e.g., KNN/Iterative Imputer) are applied to optimize variable efficiency and handle
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with ORSR. This PhD explores how fun operates as a core experiential concept shaping the quality of youth sport. Using both qualitative and quantitative methods, it will map the dimensions of fun as
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using quantitative and qualitative methods. The candidate will be supported by experts in cardiovascular disease, clinical trials, and implementation science research. This PhD project may involve
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, the work package associated with this position is led by Professor Mark Dras in the School of Computing, within the Faculty of Science and Engineering. Professor Dras leads a very active AI research
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path from a start to a goal location without colliding with each other. The optimisation group at Monash is leading research in this area and has designed some of the most efficient methods to solve
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improve their accuracy and robustness. Apart from proposing and implementing deep learning methods for detection purposes, this project also involves collecting and/or generating different datasets for
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path from a start to a goal location without colliding with each other. The optimisation group at Monash is leading research in this area and has designed some of the most efficient methods to solve
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improve their accuracy and robustness. Apart from proposing and implementing deep learning methods for detection purposes, this project also involves collecting and/or generating different datasets for