15 – 17 Nov 2024
Conference, Symposium, Forum
Molecular Frontiers Symposium in Hong Kong at HKUST with Nobel Laureates
INTRODUCTION The Molecular Frontiers Symposium, organized by the globally renowned Molecular Frontiers Foundation - founded by the Royal Swedish Academy of Sciences is recognized as one of the most influential scientific organizations worldwide. For the first time in the…
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6 Aug 2020
PhD in Chemistry - Kinetic and Structural Investigation on Mechanism-Based Inhibition of the Essential Thiamine-Dependent Enzyme in Vitamin K2 Biosynthesis
Candidate: Mr. LI, Tin Lok
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6 Aug 2020
Other Format
PhD in Chemistry - Kinetic and Structural Investigation on Mechanism-Based Inhibition of the Essential Thiamine-Dependent Enzyme in Vitamin K2 Biosynthesis
Candidate: Mr. LI, Tin Lok
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30 Jul 2020
Seminar, Lecture, Talk
CHEM - PhD Student Seminar - Symmetry Breaking Effect Beyond Molecular Level: from Achial Molecules
Student: Mr. Wenjie WU Department: Department of Chemistry, HKUST Supervisor: Professor Benzhong TANG
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30 Jul 2020
PhD in Chemistry - Elucidating Molecular Mechanism of Functional Conformational Change of Protein through Molecular Dynamics Simulations and Markov State Model
Candidate: Miss Xiaowei WANG Department: Department of Chemistry, HKUST
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29 Jul 2020
Seminar, Lecture, Talk
CHEM - PhD Student Seminar - Circularly Polarized Light from Organic Materials
Student: Miss Michidmaa KHORLOO Department: Department of Chemistry, HKUST Supervisor: Professor Benzhong TANG
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29 Jul 2020
Seminar, Lecture, Talk
CHEM - PhD Student Seminar - Recent Progress of NIR-II Fluorescent Materials for Bioapplications
Student: Miss Wenhan XU Department: Department of Chemistry, HKUST Supervisor: Professor Benzhong TANG
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27 Jul 2020
PhD in Chemistry - Charge Transport Study and Device Fabrication for Efficient Non-Fullerene Based Indoor Organic Solar Cells
Candidate: Mr. Lik Kuen MA Department: Department of Chemistry, HKUST
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24 Jul 2020
Seminar, Lecture, Talk
MATH - Seminar on Statistics and Data Science - Generalization error of linearized neural networks: staircase and double-descent
Deep learning methods operate in regimes that defy the traditional statistical mindset.
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