499 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at National University of Singapore
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or Teaching Assistant at the National University of Singapore (NUS) Are you passionate about data science, machine learning, and artificial intelligence? Do you enjoy teaching and empowering others with
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enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning approaches for biomarker
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multimodal data, enabling early detection and personalised interventions in clinical neuroscience. The candidate will take the lead on machine learning and computational analyses, primarily supporting our
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possess strong interpersonal communications skills with excellent written, oral communication and computer skills. Perform experiments with live laboratory mice, including administering treatments
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Qualifications Education: - Doctoral degree in Computer Science, Biomedical Informatics, Data Science, Artificial Intelligence, Machine Learning, Educational Technology, or closely related field - Postdoctoral
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/Senior Lecturer at 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
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Location_ONB: Kent Ridge Campus Posting Start Date: 31/10/2025 Job Description The successful candidate will work with Associate Professor Duane Loh on conducting research at the interface of Machine Learning
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efficiency by learning from and collaborating with other clinical trial networks. In the long term, the Network aims to support broader infectious disease studies by expanding geographically beyond its
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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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of AI / deep learning / machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out