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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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capillary pressure, and data-driven, physics-driven machine-learning. Applications are sought for a two-year postdoc position, and will work closely with an industry partner. The position will include a
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. The successful candidate will contribute to understanding how modern machine learning models represent information internally and how their predictions can be made transparent, reliable, and clinically meaningful
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the group of Dr Yasir Noori. In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post-fabrication device characteristics from process
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on computer vision. The role will focus on developing, designing, and implementing novel algorithms and models to address emerging problems in computer vision, such as Multimodal Large Language Models and
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. The successful candidate will contribute to understanding how modern machine learning models represent information internally and how their predictions can be made transparent, reliable, and clinically meaningful
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, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures
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the group of Dr Yasir Noori. In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post-fabrication device characteristics from process
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, and interpretability of quantum-inspired models against classical machine learning methods. Develop a prototype system integrated with learning management system (LMS) data (e.g., Moodle) to demonstrate
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of collaborators spanning CERM, NUS, Imperial College London, Ashoka University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global