41 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions at National University of Singapore
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Job Description Job Alerts Link Apply now Job Title: Research Analyst/ Associate/ Fellow in Machine Learning and Artificial Intelligence (ML/AI) Posting Start Date: 12/09/2025 The Role
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international collaborators across clinical, academic, and industry settings to develop privacy-preserving machine learning approaches, federated learning frameworks, and interpretable algorithms for multimodal
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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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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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including functional enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning
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statistical and machine learning modeling to conduct data analyses for large-scale multimodal (genomics, omics etc) studies. Conceptualise new ideas, lead data-driven discoveries, ensuring in-depth assessment
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. • The ability to work independently and collaboratively within a multidisciplinary team. • Strong writing, critical thinking, communication, and presentation skills. • Experience in Machine Learning is a
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experience with management and strategy research, economic modelling and statistical analyses are essential. Familiarity with data mining, machine learning and computation techniques, especially in the context
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Health, Environmental Health, Biological Sciences, Biostatistics, Data Science, preferably with relevant experience. Prior experience with machine learning is a plus. Recruitment is open immediately and
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for machine learning and artificial intelligence, with a strong emphasis on developing and applying models such as LSTM and other time-series analyses to predict the longevity and behaviour of bioactive