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Doctor of Philosophy (PhD) in Machine Learning for In Situ Materials Characterisation Job No.: 698995 Location: Clayton campus Employment Type: Full-time Graduate Research Degree: 3291 - Doctor
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This PhD research aims to develop and evaluate explainable AI and machine-learning approaches for early detection, risk stratification and prognostic prediction of colorectal cancer, with a focus on
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energy availability, electricity prices, charging infrastructure and battery status. It will explore spatiotemporal data management, optimisation and machine learning techniques for individual EVs
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Scholarship ($37,000 per annum). The stipend rate is indexed annually and published on the Monash University Graduate Research Stipend and Allowance Rates website. The Opportunity: This PhD stipend scholarship
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, experimental frameworks and evaluation methodologies. We are seeking someone with a PhD or near completion in Artificial Intelligence, Computer Science, Machine Learning or a closely related discipline, together
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) to improve cardiovascular risk assessment and prediction. Research Aim This PhD research aims to develop and evaluate AI and Machine Learning approaches for early prediction, risk stratification and prognostic
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for Artificial Intelligence (AI) and Machine Learning (ML) to support earlier and more personalised healthcare. Research Aim This PhD research aims to develop advanced AI and Machine Learning methods
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and AI PhD candidate interested in agentic AI, supervised by Dr Teresa Wang and Dr Tongtong Wu at Monash University, jointly with Dr He Zhao and Dr, Dan Steinberg from CSIRO, Dr Yue Yang and David
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data provides opportunities for advanced Machine Learning (ML) approaches. Research Aim This PhD research aims to develop advanced Machine Learning methods for prediction, risk stratification, clinical
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-edge research under joint supervision from both world-class institutions, culminating in a recognized double PhD degree. Monash University (Australia) Ranked 31st globally and a member of Australia’s