Tencent Quantum Lab
Tencent Quantum Lab aims to connect fundamental theory with practical applications in the fast-growing sector of quantum information technology.
In close collaboration with leading universities, research centers, and companies around the world, we build and develop innovative quantum algorithms, systems, software, and cloud services for artificial intelligence and quantum chemistry.
Laboratory Director
Shengyu Zhang, Distinguished Scientist in Tencent; Associate professor, Department of Computer Science and Engineering (CSE) at The Chinese University of Hong Kong (CUHK)
Shengyu Zhang obtained his B.S. in mathematics, Fudan University in 1999, his M.S. in computer science, Tsinghua University in 2002 (under the supervision of Prof. Mingsheng Ying). And obtained his Ph.D. in computer science, Princeton University in 2006 (under the supervision of Prof. Andrew Chi-Chih Yao). After working in NEC Laboratories America as a summer intern, he moved to California Institute of Technology for a two-year postdoc, under the supervision of Prof. Alexei Kitaev, Prof. John Preskill, and Prof. Leonard Schulman.
The Team
We are an interdisciplinary team developing on Quantum Computing Engineering, Computing Chemistry, Quantum Algorithms, Electronic Engineering, Machine Learning...
A team with worldwide education backgrounds: Caltech, Cambridge, MIT, NTU, NUS, Ottawa, Princeton, TU-Delft, U. Bristol, U. Paris 7, USC; CUHK (China), PKU (China), USTC (China), ...
A team with majors on Mathematics, Physics, Computer, Electronic Engineering, Chemistry...
Contact Us
13F of Malata Building, Kejizhongyi Avenue, Hi-tech Park, Nanshan District, Shenzhen
Tel: 0755-86013388 Ext. 47681
Mailbox: qlab@tencent.com
Join Us
Contact us via qlab@tencent.com
Job Descriptions:
Use machine learning techniques to model quantum mechanical systems with a particular focus on chemistry related applications.
- Build a regression model to predict molecular properties.
- Build a generative model for drug design and discovery.
- Build a reinforcement-learning based model to perform retrosynthesis.
Qualifications:
- Master degree or higher in Computer Science, Applied Math, Aritificial Intelligence, Automation and Control, Statistics and Computational Chemistry preferred.
- 3+ years' experience of Machine Learning and relevant fields: Graph Neural Network, Generative Model, Deep Learning, Pattern Recognitions, Probability and Statistics, Optimization Methods.
- Expereince in C/C++ or Python with a strong track record of machine learning implementations using common frameworks such as DGL,Pytorch and Tensorflow etc.
- Previous publications in top conference preceedings such as NIPS, ICML, ICLR, IJCAI, AAAI, CVPR, ICCV, KDD etc, or demonstration of key contributions to major open source projects are highly preferred.
- Extensive experiences in large-scale machine learning projects, strong problem-solving and analytical skills, excellent reading and writing skills in English, and ability to conduct high-quality original research.
Quantity Required: 2