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- XIAN JIAOTONG LIVERPOOL UNIVERSITY (XJTLU)
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- UNIVERSITY OF MACAU
- Beijing Normal-Hong Kong Baptist University
- Chinese Institute for Brain Research, Beijing
- Hong Kong University of Science and Technology(Guangzhou)
- Jinan University - University of Birmingham Joint Institute
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- UNIVERSITY OF NOTTINGHAM NINGBO CHINA
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Skills: • Prior experience in quantum information, quantum computing, machine learning. • Proficiency in computer programming matlab, python, mathematica. How to Apply and required documents: • Personal CV
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Equations & Applications Data Science, Machine Learning, and AI Mathematical Materials Science and other emerging fields in applied mathematics Qualifications: A PhD (completed or expected by the date
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-driven Drug Discovery/Biomaterials/Life Science Applications Cognitive Intelligence and Agentic AI Research Centre (CIAA) Pattern Recognition/Machine Learning/Deep Learning/Reinforcement Learning, LLM/MLLM
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(CIAA) Pattern Recognition/Machine Learning/Deep Learning/Reinforcement Learning, LLM/MLLM, Generative AI, Neural Graphics, Graph Mining, Data Mining, AI agent, Natural Language Processing, Computer
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(normally PhD) degree in electrical and electronics, mechatronics, intelligent controls, robotics engineering, computer science, artificial intelligence, computer vision, mobile robotics, machine learning
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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fluid mechanics, computational geometry, meshing, computational graphics, computational vision, or scientific machine learning in general. Successful candidates will join a community of researchers in
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that integrate physical laws, numerical solvers, and machine learning, with strong emphasis on interpretability, reliability, and scientific validity. The position will contribute substantially to both high
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of interdisciplinary AI and data science are also encouraged to apply: (1) AI for Science, including AI/ML for scientific discovery, foundation models for science, scientific machine learning, and AI applications in
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technologies in education: Artificial Intelligence in Education Learning analytics, data science, and educational data mining Machine learning applications in educational contexts Technology-enhanced learning