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) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena
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are looking for a Research Fellow to support the research projects. The research fellows will work closely with the other institutes to integrate their systems into the processing of fibrous muscles. Key
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for Quantum Technologies (CQT) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices
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instrumentation and sample processing workflows Ability to work independently, manage multiple projects, and contribute actively to collaborative research Excellent organizational skills and attention to detail
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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, and IRB applications. Phase 3 – Data collection and pre-processing. Phase 4 – Data analysis and result tabulation. Phase 5 – Research communication and writing of papers. The level of responsibility and
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for measuring patient reported outcome measures in the population of Singapore based on the efficient application of Natural Language Processing and Item Response Theory to existing measurement systems working
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addition to annual leave, the Research Fellow may apply for leave to undertake research and fieldwork overseas, subject to the approval of the CIL Director. Application Procedure Application should be submitted online
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-Computer Interaction, Health Informatics, Data Science, or a related field. • Demonstrated experience in the design and evaluation of digital health or mHealth interventions, ideally involving real-time data
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) the development of supervised and unsupervised machine learning tools to process information from various data sources, including commercial databases and alternative, non-traditional datasets. Plan, supervise and