699 systems-science "https:" "https:" "https:" "https:" "U.S" positions at Monash University in Australia
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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them
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The Koto Dream Scholarship Achieving Potential International Scholarship The Koto Dream Scholarship is designed to support outstanding Know One, Teaching One (KOTO) alumni who demonstrate strong
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Synthetic data generation has drawn growing attention due to the lack of training data in many application domains. It is useful for privacy-concerned applications, e.g. digital health applications
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strong sense of justice. As an alumni of Monash University (Mechanical Engineering), Michael was one of four engineers who established Vipac Engineers and Scientists Ltd in 1973. Today, Vipac is an
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been directly observed in planet forming discs around young stars (protoplanetary discs) and is inferred to be occurring around black hole discs. My research projects use a combination of 3D and 1D
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AI is now trending, and impacting diverse application domains beyond IT, from education (chatGPT) to natural sciences (protein analysis) to social media. This PhD research focuses on the fusing AI
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, Alice has no choice but to give away her highly sensitive information. A more ideal solution is to use a PET tool to provide Alice a way to (cryptographically) prove to SerPro that she is eligible
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Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation
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Generative AI NLP skills System security Software testing To be eligible you must have: A first-class honours (H1) Bachelor’s degree or equivalent in the relevant research area (completed or near
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to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from