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Field
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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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programming language Experience with statistical inference or machine learning methods (e.g. ABC, Bayesian modelling) A proven publication record with at least one first author publication in a peer-reviewed
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods
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/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
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expertise in the following areas: Machine Learning in general, with an emphasis on deep learning and language modeling Model benchmarking and evaluation pipelines for NLP/LLMs Domain-aware application of AI
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and development activities in the fields of atomistic simulations, including density functional theory, machine learning, and molecular dynamics. The work involves theoretical and experimental research
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, machine learning, and life cycle assessment, we aim to create sustainable wearable systems to enhance human well-being. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking
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a postdoctoral candidate in the area of federated learning and wireless communications. The candidate must hold (or about to complete) a PhD in the related fields. The candidate will be involved in a
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repeated for a database of events covering different sea ice types, conditions, locations, and rates of ice deformation (from docile to violent). Machine learning techniques will then be used to find a
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disciplines, including human-robot interaction, robot learning, soft robotics, computer vision, and agricultural robotics. About the PhD project: We are looking for a highly motivated and talented PhD research