702 systems-science-"https:" "https:" "https:" "https:" "UCL" positions at Monash University
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community Be surrounded by extraordinary ideas - and the people who discover them The Opportunity The Department of Electrical and Computer Systems Engineering at Monash University is seeking a Level A
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In collaboration with people from Monash materials engineering, neuroscience and biochemistry we are developing living AI networks where neurons in a dish are grown to form biological neural
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Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but
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background in AI/ML, data science, or signal processing Interest in music informatics, emotion modelling, or multimodal AI Ability to implement and evaluate machine learning models independently Commitment
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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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, 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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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