1,470 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" positions in Singapore
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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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/Associate to support a university research collaboration on applying machine learning to predict IVF treatment outcomes using health-check data and Chinese clinical reports. The role will focus
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: https://careers.nus.edu.sg/job/Research-Assistant-%28LKYSPP%29-%2828582%29/28582-en_GB/ Research Associate apply here: https://careers.nus.edu.sg/job/Research-Associate-%28LKYSPP%29-%2828583%29/28583
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or personal mentorship; capture feedback on knowledge sharing and technical problem-solving impact. Identify real-world AI projects that build software engineering depth and machine learning pipeline skills
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research takes place at the boundary of quantum optics, particle waveshaping and nanomaterials. Read more about our work at our group website https://sites.google.com/view/ljwgroup . Key Responsibilities
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: Kent Ridge Campus Posting Start Date: 25/03/2026 Job Description The National University of Singapore invites applications for Research Assistant for “Learning about Our behavior is Valuable
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) 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 this new discipline of
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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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/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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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