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Field
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learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial analysis Strong publication record Experience in women and children’s
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for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries of performance, speed, and precision
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instructing or supervision of labs and tutorials. Prior teaching experience is preferred for candidates. It would be good if the candidate can also explore areas such as multimodal algorithms/techniques
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with extremely weak couplings, such as light dark matter, axions, and gravitational waves, by pioneering the weak coupling frontier through the use of cutting-edge quantum sensors and devices. QUP
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to the development of innovative and sustainable low carbon plastic waste management and recycling solutions. In this project the post holder will develop novel algorithms and methods for analysis of plastic data
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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Analytics & Explainability development of algorithm and system for the project The School of Computing at the National University of Singapore (NUS) invites applications for a Postdoctoral Research Fellow
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algorithms, and innovative mechanical design to replicate the remarkable flight capabilities observed in nature. The successful candidate will be part of a dynamic, multidisciplinary team of experts in
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. The successful applicant will use state of the art inference algorithms to design, use and share the findings of epidemiological models that integrate across large and diverse datasets including capture-mark
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. Experience in matlab, MEEP, RCWA and other softwares and algorithms. Excellent publication track records. Good understanding of nonlinear and quantum optics. Strong knowledge of optical physics, while