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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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digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different conditions. To propose a methodology/framework in a
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expression, cell state-specific regulatory programs, and clinical outcomes. Related projects will include: Develop and apply statistical or machine learning approaches to model the effects of common and rare
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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with machine-learning–enhanced statistical inference. This position is based at the University of Otago in Dunedin and will be managed/supervised by Dr Fabien Montiel. The postdoctoral fellow will lead
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nonstationarity, graph theory, machine learning and multivariate statistics; with applications in neuroscience, climate research, economics and general communication networks. More information about the group
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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conferences in areas of machine learning, computer vision, and Large Language Models and high-impact specialist peer reviewed academic journals. • Ability to conduct interdisciplinary research activities
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Engineering in the 2025 QS World University Rankings by Subjects. The EEE Rapid-Rich Object SEarch (ROSE) Lab focuses on research in: (i) visual search & retrieval, (ii) video analytics & deep learning, and
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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative