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-machine teaming. Moreover, the consequences of such digital transformation are explored in the context of workforce and labor developments, organisational innovation, and learning/education. About the
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and intelligent networked systems, including theoretical and system-level research Demonstrated capability in advanced communication technologies, antenna systems, and machine learning–enabled methods
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is to develop real-time truck-drone collaboration system to enhance Singapore safety and resilience and using learning-based approaches. Key responsibilities include: Collecting and analyzing relevant
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Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different
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for machine learning and artificial intelligence, with a strong emphasis on developing and applying models such as LSTM and other time-series analyses to predict the longevity and behaviour of bioactive
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aquaculture (e.g., behavioral analysis, growth prediction, digital twin, computer vision.) Develop, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets
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acquired from the field survey Develop machine learning models for prediction and recommendation Job Requirements: Preferably PhD in Computer Science or related field. Expertise in computer programming
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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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equivalent. Strong background in machine learning and computer vision. Prior experience in data-efficient classification, synthesis, and detection is preferable. Strong publication records in top-tier machine
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background/interest in time-series analysis, theoretical machine learning on networks, and high-dimensional statistics. Key Responsibilities: Take the lead in developing sub-projects (problem formulation