819 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions in Singapore
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Responsibilities: Conduct research in Systems Biology to study cell signaling Perform large-scale signaling analysis or protein-interactome analysis Utilize machine learning-based biological data mining and analysis
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/or computational skills (e.g., large-scale data analysis, bibliometric analysis, machine learning methods) will be an advantage. Excellent written and oral communication skills. We regret that only
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, machine learning, AI, computer vision, large language models, Agentic AI or human–computer interaction. Excellent oral and written communication skills. Resourceful and up-to-date with developments in
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/3D animation studios. This role is central to enabling high-quality, future-ready teaching, learning, and research across animation and adjacent disciplines such as Interactive Media, Game Design, and
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to groom the next generation of leaders, thinkers, 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
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researchers, and the opportunity to make a meaningful impact on the early detection and prevention of cognitive disorders. Assist in developing and validating machine learning-based predictive models using
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computational analyses (e.g., regression, network analysis, machine learning). Academic writing skills and willingness to co-author publications. Strong problem-solving skills and ability to work independently
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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, immunology and molecular biology is required. Able to travel occasionally for conferences, project sites and meetings with collaborators. Possess strong interpersonal, communication, computer, writing and
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on Thurlings et al.’s models of feedback processes, most feedback in computer systems is cognitivist in nature. The advancements in LLMs appear promising in bridging this dialogic gap in feedback and learning