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Primary supervisor Levin Kuhlmann Research area Machine Learning What makes a machine conscious? This PhD would be at the intersection of Philosophy, AI and neuroscience. You would study the latest
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Primary supervisor Levin Kuhlmann Research area Machine Learning What makes a machine conscious? This PhD would be at the intersection of Philosophy, AI and neuroscience. You would study the latest
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specialised as-needed via fine-tuning or prompt engineering. In this project we will explore all aspects of this process with a focus on increasing trust in the model outputs by reducing or eliminating
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specialised as-needed via fine-tuning or prompt engineering. In this project we will explore all aspects of this process with a focus on increasing trust in the model outputs by reducing or eliminating
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." Advances in Neural Information Processing Systems, 2019. Required knowledge Eligibility and Requirements: Domestic students (Australian PR or Citizen) for PhD, Masters, and Honors; Meet the entry
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
ensure local relevance. Required knowledge Artificial Intelligence; Machine Learning; Computer Vision; Bioinformatics; Biomedical Engineering; Neuroscience; Genomics; Medical Physics; Data Science
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Maxwell King PhD Scholarship The Maxwell King Scholarship (MKS) is named after Professor Maxwell King, who has made an outstanding contribution to graduate research at Monash University, including
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Primary supervisor Mor Vered Co-supervisors Dana Kulic Leimin Tian Research area Data Science and Artificial Intelligence This PhD project will investigate the explainability of reinforcement
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Primary supervisor Mor Vered Co-supervisors Dana Kulic Leimin Tian Research area Data Science and Artificial Intelligence This PhD project will investigate the explainability of reinforcement
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Intelligence (AI) and Machine Learning (ML) to colorectal cancer research. Research Aim This PhD research aims to develop and evaluate explainable AI and machine-learning approaches for early detection