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- XIAN JIAOTONG LIVERPOOL UNIVERSITY (XJTLU)
- Duke Kunshan University
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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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] Subject Areas: Data Science Computer Science / Artificial Intelligence , Artificial intelligence and machine learning , Artificial Intelligence, Machine Learning, Large Language Models , Artificial
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China invites applications for a Professor / Associate Professor in Econometrics with a strong specialization in Machine Learning and Data Science. The appointment is expected to begin in August 2026
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approach, combining machine-learning–enhanced text-as-data analysis with qualitative discourse analysis. The project aims to produce a set of high-quality scholarly outputs, including peer-reviewed journal
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Areas: Data Science / Computer Science , Machine Learning Computer Science / Artificial Intelligence , Computer Architecture , Embedded Systems, Edge Computing, and Mobile Computing Systems , Human
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+ AI framework, and the continued development of new academies to build an institutional ecosystem. With a focus on innovative learning and teaching, and research, XJTLU draws on the strengths of its
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multiple symbolic fields, including official political discourse, academic debates and selected cultural outputs. Methodologically, it adopts a mixed-methods approach, combining machine-learning–enhanced
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engineering, compiler, computer architecture, machine learning, artificial intelligence, human-machine interaction, and embedded systems. Candidates with interdisciplinary background (e.g., at the intersection
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, Operations Research, Data Science, Machine Learning and Artificial Intelligence, Statistics, Management Science, and other closely related areas. We especially encourage individuals whose interests and
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are expected to have a Ph.D. in theoretical particle physics or related areas prior to the time of employment. Preferences will be given to those with experiences in collider phenomenology, machine learning