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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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Centre for Lifelong Learning and Individualised Cognition CLIC is a research collaboration between the University of Cambridge and Nanyang Technological University (NTU) and hosted by the Cambridge
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. Job Requirements: Preferably PhD in Computer Science or related field. Background and familiarity with the implementation and deployment of machine learning pipelines in embedded systems (e.g., robotic
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into operational use cases. Prepare data collection frameworks and work on fish health monitoring datasets for machine learning training and benchmarking. Support the development of translational “lab-on-farm
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., • Interest in developing risk prediction models via deep learning/machine learning. • Have strong background in DL, EEG data and programming for the implementation of proposed methods. Apply now
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on incentive mechanism design. Help supervise PhD, master and undergraduate students, and R&D staff. Help with federated learning technical platform design and development. Help with collaboration with existing
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. Qualifications/Requirements Qualifications / Discipline: - PhD from a reputable institution in Physics, Bio-imaging, Computer Science, or a scientific domain closely related to Machine Learning. - The candidate
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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. Experts in
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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. Experts in