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molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials. The research
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Participate in seminars, workshops, and conferences related to combinatorics and graph theory The successful candidate will hold a PhD in Mathematics or a closely related discipline, with a strong background in
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candidate will hold a PhD in Mathematics or a closely related discipline, with a strong background in edge decomposition of graphs or fractional structures in graphs. You will have the ability to solve
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driven inverse design of functional materials. Current research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models
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representative work samples (e.g., journal paper, code repository, technical report, ontology/graph model) • 4 Reference letters, including one from candidate’s PhD supervisor • PhD/Master/Bachelor degree
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Responsibilities: Research and develop novel ML-based methodologies and algorithms in LLM-empowered Sub-Graph Learning for Large Graph Models. Working closely with other Postdoc/RA/PhD students to discuss the ideas
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complex biological processes. This project combines timely analytical challenges with deep rooted applications in life science. We are looking for a candidate with a PhD in either engineering/computer
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What You’ll Need: PhD in computer science, artificial intelligence, machine learning, computational biology, biomedical engineering, or a closely related quantitative field. Strong foundation in modern
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on energy-efficient circuit design and software-hardware co-optimization, with exciting applications in graph-based prediction. What we’re looking for: A PhD in Electrical and Computer Engineering or a
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal