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- XIAN JIAOTONG LIVERPOOL UNIVERSITY (XJTLU)
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/EXPERIENCE: Numerical machine leaning/Scientific Machine Learning are highly expected Preferred research directions: Numerical Optimization and Scientific Machine Learning. A PhD in Mathematics or Applied
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particle physics or related areas prior to the time of employment. Preferences will be given to those with experiences in collider phenomenology, machine learning, effective field theories, positivity bounds
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as an integrated analytical framework, we apply comparative risk assessment, disease modeling, machine learning, and survival extrapolation methods to systematically quantify the long-term impacts
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science, artificial intelligence, computer vision, mobile robotics, machine learning, data science and analytics, or be able to demonstrate an equivalent professional practice and engagement. Previous experience in
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at the forefront of research that integrates modern machine learning methods with economic theory and econometric analysis. We are particularly interested in individuals whose work addresses substantive economic
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Charter of Macao Polytechnic University and be able to teach Machine Learning, Big Data Analytics, Learning Theories and Technology Integration, Innovations in Technology and Education, Thesis, etc. (taught
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experience, contributing to curriculum design, teaching, assessment, and educational research. They will bring expertise in contemporary pedagogies, including co-creation and active learning, and demonstrate
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., Computational Mathematics, Computer Science, Economics, Finance, Financial Engineering, Information Systems, Machine Learning, Mathematical Finance, Operations Research, Optimization, Probability, And Statistics
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Professor level. The ideal candidate will be at the forefront of research that integrates modern machine learning methods with economic theory and econometric analysis. We are particularly interested in
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, assessment, and educational research. They will bring expertise in contemporary pedagogies, including co-creation and active learning, and demonstrate effective practice in digital education and online