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an emphasis on technology, data science and the humanities. We are looking for a Research Fellow to conduct AI for medicine research. The role will focus on developing foundation models to medical image
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of imaging this highly dynamic and complex organelle. Our lab has developed various quantitative and super-resolution imaging tools specifically designed for studying the Golgi, including en face averaging
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Join Our Team at the School of Biological Sciences, Nanyang Technological University, Singapore The School of Biological Sciences (SBS), part of the College of Science, was established in 2002 with a mission to advance biological and biomedical sciences. At SBS, our research spans various...
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on emerging privacy-preserving techniques such as homomorphic encryption, secure multi-party computation and federate learning. Key Responsibilities: Conduct advanced research in the areas of privacy-preserving
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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imaging, in vivo models and multi-omics technologies to elucidate pathways critical to type 2 diabetes mellitus pathogenesis, with focus on pancreatic and immune function. The team is moving into molecular
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the research on multi-layer metasurfaces for wireless communications, sensing, and computing. Key Responsibilities: Design the multi-layer metasurfaces prototype for wireless communications, sensing
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health assessment through visual behavior recognition, image-/video-based analytics, and biosignal interpretation (e.g., growth rate estimation, movement, coloration). Design and implement experiments
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems