329 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at National University of Singapore
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adaptive decision systems. Contribute to research publications, technical reports, and open-source toolkits. Collaborate with faculty, postdoctoral researchers, and students on advanced machine learning
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the National University of Singapore (NUS) Are you passionate about shaping the future of data literacy? Do you have a strong background in AI and Machine Learning, with a commitment to empowering others with
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attitude and a keen interest in learning and growing within the role. Equipped with effective organisational and interpersonal communications with excellent written, oral communication and computer skills
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methodologies to analyse multimodal data, enabling early detection and personalised interventions in clinical neuroscience. The candidate will take the lead on machine learning and computational analyses
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by reporting officer Qualifications Education: - Doctoral degree in Computer Science, Biomedical Informatics, Data Science, Artificial Intelligence, Machine Learning, Educational Technology, or closely
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foundation in areas such as data analysis, machine learning, AI, computer vision, large language models, Agentic AI or human–computer interaction. Excellent oral and written communication skills. Resourceful
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Loh on conducting research at the interface of Machine Learning and Microscopy under a project on Learning Spatiotemporal Motifs In Complex Materials. The main responsibilities of the position include
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operational modelling and simulation, pricing & cost control to contribute to growth of the team resources. The ideal candidate is highly organized and proficient in using current data and machine learning
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or personal mentorship; capture feedback on knowledge sharing and technical problem-solving impact. Identify real-world AI projects that build software engineering depth and machine learning pipeline skills
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through the application of AI / deep learning / machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data