1月20日
研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - A Journey to Defect Science and Engineering
  Abstract A defect in a material is one of the most important concerns when it comes to modifying and tuning the properties and phenomena of materials. The speaker will review his study of defects over the course of his professional career, reflecting on his journey through the…
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6月24日
研讨会, 演讲, 讲座
Seminar of Physics Department - Condensed Matter Seminar: How do we discover Majorana particles in nanowires?
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6月21日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - A three-dimensional unified gas-kinetic wave-particle solver for flow computation in all regimes
In this talk, the unified gas-kinetic wave-particle (UGKWP) method will be presented for the three-dimensional multiscale flow simulations.
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6月18日
研讨会, 演讲, 讲座
Department of Chemistry - PhD Student Seminar - Raman Spectroscopy for Cancer Diagnosis
Student: Miss Hongni ZHU Department: Department of Chemistry, HKUST Supervisor: Professor Jinqing HUANG
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6月18日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - Longtime existence of the L2-gradient flow of the Hawking mass functional
One of the important notions of quasi-local mass in general relativity is the one proposed by Hawking in 1968, nowadays commonly known as the Hawking mass.
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6月17日
研讨会, 演讲, 讲座
Department of Chemistry - Seminar - Developing Accurate and Efficient Force Fields to Model Phase-transfer Catalysis Near Liquid-liquid Interfaces
Speaker: Professor Ying-Lung Steve TSE Institution: Department of Chemistry, The Chinese University of Hong Kong Hosted By: Professor Xuhui HUANG
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6月17日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Pure Mathematics - Twisted S-duality
String theory is a framework of physics which in particular gives rise to various interesting predictions in mathematics.
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6月10日
研讨会, 演讲, 讲座
Machine Learning Complex Dynamics
Abstract
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6月9日
研讨会, 演讲, 讲座
MATH - Seminar on Statistics and Data Science - Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
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