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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community
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Materials Generative Design and Validation Framework. The role will work at the intersection of machine learning, high-throughput experimentation, and materials discovery, focusing on accelerating the design
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diffusion models using path integral formulations. This project aims to advance quantum machine learning by: Designing a quantum counterpart of diffusion models; Leveraging path integral methods to model
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Are you keen to pioneer machine learning models that address the challenges of robot perception? We are recruiting a research fellow who will work on our EPSRC-funded research project on “Active
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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, leveraging advanced learning analytics, machine learning, and deep learning techniques. The candidate shall work under the supervision of the Principal Investigator (PI) and Co-PIs to conduct academic research
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machine learning/AI or cybersecurity, or both, as evidenced by a strong track record of publications in leading journals and conferences in relevant areas. Software programming skills. To be successful
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bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms Downloading a copy of our Job Description Full
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postgraduate-level research in Computer Science, Cybersecurity, Information Security, Information Technology, Artificial Intelligence, Machine Learning, or a related field, have experience with securing AI
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Large Language Models Natural Language Processing (NLP) Deep Reinforcement Learning Theoretical Foundations of Deep Learning and AI Optimization Techniques in Machine Learning Collaboration and Mentorship