776 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions in Singapore
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of lab-scale and pilot-scale experiments/operations with element of machine learning, sample characterizations, data analysis, and so on. Job Requirements: PhD degree in Materials Science, Polymer Science
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or Teaching Assistant at the National University of Singapore (NUS) Are you passionate about data science, machine learning, and artificial intelligence? Do you enjoy teaching and empowering others with
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candidates with a deep passion for AI and LLM-related research, and their applications in the financial domain. The selected candidate will play a key role in advancing our machine learning, large language
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applications for a Visiting Professor position in Computational Statistics, Statistical Signal Processing, Bayesian Inference, or Machine Learning. Candidates should hold a Ph.D. in statistics, mathematics
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. • Familiarity with reinforcement learning and more generally machine learning. • Experience with communication protocols such as LoRaWAN, Zigbee, BACnet, Modbus, and IoT integration using MQTT and RESTful APIs
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, machine learning and natural language processing techniques. Designing and implementing empirical research strategies, including quasi-experimental methods, risk measurement frameworks, and policy
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the area of efficient and interpretable machine learning systems. The successful candidate will work on projects involving ensemble learning, large-scale data analytics, and high-performance model design
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foundation in areas such as data analysis, machine learning, AI, computer vision, large language models, Agentic AI or human–computer interaction. Excellent oral and written communication skills. Resourceful
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such as AI-enhanced translation workflows, Neural machine translation, large language models, or multilingual NLP, Multimodal and multilingual AI, Translation quality evaluation using AI Teach undergraduate
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and the associated vulnerability patterns in aging and neuropsychiatric disorders using multimodal neuroimaging and machine learning methods. We are interested in the large-scale brain structural and