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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
knowledge Artificial Intelligence; Machine Learning; Computer Vision; Bioinformatics; Biomedical Engineering; Neuroscience; Genomics; Medical Physics; Data Science; Biotechnology Project funding Project based
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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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motivation Testing whether the system performs equitably across cultural and language groups Training spans intervention design, trial methodology, human-computer interaction, and applied machine learning
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PhD candidate will have: A strong background in computer science, artificial intelligence, machine learning, or a closely related field. A solid understanding of machine learning concepts, particularly
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Required knowledge Skills Focus proficiency in one programming language (e.g. Matlab, R, Python), machine learning / deep learning / data science skills, basic understanding of cell and development
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publications in established machine learning, computer vision, artificial intelligence, and robotics venues. Depending on the contribution and maturity of the work, relevant conferences include NeurIPS, CVPR
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, data processing pipelines, machine learning and generative AI applied to physical activity and sleep research. The position will contribute to the development of an AI-based behaviour change tool and
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, privacy-preserving technologies, adversarial machine learning, explainable AI, or secure software engineering. Collaborate and Translate: Work within a supportive team, present findings at national and
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the guidance of senior lawyers across a wide range of matters arising from teaching and learning and research activities and national and international collaboration initiatives, as well as day to day legal
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of foundation models in natural language processing and computer vision, this project seeks to develop general-purpose graph foundation models capable of learning transferable representations from large-scale