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. Machine-learning and deep-learning techniques will be developed and compared for cancer-risk prediction, classification and prognostic modelling. Explainable and Multimodal AI Explainable AI (XAI) methods
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and, where appropriate, medical imaging and longitudinal data. A range of machine-learning and deep-learning techniques will be developed and evaluated for cardiovascular disease prediction and risk
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, lifestyle and, where available, longitudinal patient data. Machine-learning and deep-learning methods will be developed and evaluated for early prediction, risk stratification and prognostic modelling
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
these engagements will be integrated with outputs from advanced AI models (e.g.,geospatial deep learning, graph neural networks, and explainable AI ) to refine the AMR risk maps and ensure local relevance. Required
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materials discovery with cutting-edge high-throughput platforms, robotics, machine learning, and autonomous experimental workflows. Access World-Class Facilities: Based in the Department of Chemical and
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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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deep learning and large language models, is desirable. Experience with Python and common machine learning frameworks (such as PyTorch or TensorFlow) is highly valued. Familiarity with topics such as
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This project involves the automated generation of textual descriptions for audio content, such as spoken language, sound events, or music. This process typically employs deep learning techniques
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, creating realistic audio deepfakes has become easier, raising concerns about misinformation and privacy. To combat this, this project aims to develop machine learning models to analyse audio features such as
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch