666 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions at Nanyang Technological University in Singapore
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Degree in Computer Science, Information Systems, Data Science, Software Engineering, Computer Engineering, or a related discipline. Entry level candidates are welcome to apply. Skills & Knowledge Technical
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Engineering in the 2025 QS World University Rankings by Subjects. The EEE Rapid-Rich Object SEarch (ROSE) Lab focuses on research in: (i) visual search & retrieval, (ii) video analytics & deep learning, and
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, data mining, and machine learning techniques. Familiarity with Natural Language Processing (NLP) is advantageous. Proficiency with data visualisation tools such as Qlik Sense, along with programming
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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative
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, alignment, evaluation). Design multi-MLLM collaboration methods (knowledge transfer/distillation, federated learning). Build efficient training/benchmark pipelines and report results with clear metrics. Apply
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support to the Leads and ensuring a good learning experience for the students. The Senior Executive is also required to work closely with the other departments to support major School events and activities
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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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/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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/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