博士申请 | 新加坡国立大学刘谦雄老师招收机器学习方向全奖博士/博后/RA

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新加坡国立大学

新加坡国立大学（National University of Singapore)，简称国大（NUS），是亚洲顶尖、国际知名的研究型大学（2022年排名在泰晤士高等教育世界大学排名中世界排名第21位，QS世界大学排名中排名第11位）。其电子计算机工程系（ECE）排名世界第14位（2021ARWU）。

导师简介

Dr. Bryan Low is an Associate Professor of Computer Science at the National University of Singapore and the Director of AI Research at AI Singapore. He obtained the B.Sc. (Hons.) and M.Sc. degrees in Computer Science from National University of Singapore, Singapore, in 2001 and 2002, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, Pennsylvania, in 2009. His research interests include probabilistic & automated machine learning, planning under uncertainty, and multi-agent/robot systems.

Dr. Low is the recipient of the (1) Andrew P. Sage Best Transactions Paper Award for the best paper published in all 3 of the IEEE Transactions on Systems, Man, and Cybernetics - Parts A, B, and C in 2006; (2) National University of Singapore Overseas Graduate Scholarship for Ph.D. studies in Carnegie Mellon University (CMU) in 2004-2009; (3) Singapore Computer Society Prize for Best M.Sc. Thesis in School of Computing, National University of Singapore in 2003; and (4) Faculty Teaching Excellence Award in School of Computing, National University of Singapore in 2017-2018.

Dr. Low has served as a World Economic Forum’s Global Future Councils Fellow for the Council on the Future of Artificial Intelligence and Robotics from Sep 2016 to Jun 2018 and an IEEE Robotics & Automation Society (RAS) Distinguished Lecturer for the IEEE RAS Technical Committee on Multi-Robot Systems in Mar 2019. He has served as an organizing chair for the IEEE RAS Summer School on Multi-Robot Systems in Jun 2016, the AI Summer Schools in Jul 2019 and Aug 2020, and the NeurIPS 2021 Workshop on New Frontiers in Federated Learning. Dr. Low has also served as associate editors, area chairs and program committee members, and reviewers for premier AI (specifically, multiagent systems, AI planning, robotics, machine learning) conferences: IJCAI, AAAI, ECAI, AAMAS, ICAPS, RSS, IROS, ICRA, CoRL, NeurIPS, ICML, AISTATS, ICLR and journals: TKDE, JMLR, JAIR, MLJ, TNNLS, T-ASE, IJRR, T-RO, AURO, JFR, TOSN, JAAMAS. He was the top 5% reviewer for ICML 2019, top 33% reviewer for ICML 2020, and an expert reviewer for ICML 2021.

招生信息

We are hiring postdoctoral fellows, research assistants, and Ph.D. students interested in advancing the state of the art in learning with less data (AutoML, Bayesian optimization, active learning, physics-inspired AI), with applications to automated reinforcement learning, multi-agent reinforcement learning, advanced manufacturing, and the Sciences for a period of 1 year with possible renewal/extension.

The postdoctoral fellows, research assistants, and Ph.D. students will be based in either the School of Computing of the National University of Singapore (NUS) or CNRS CREATE. The postdoctoral fellows have the opportunity to collaborate with/co-advise the PhD and undergraduate students in our research group.

For more information on our research group, interests, and recent papers in ICML, NeurIPS, ICLR, UAI, AISTATS, and AAAI, visit

https://www.comp.nus.edu.sg/~lowkh/research.html

A recorded seminar on our recent works is available here:

https://www.youtube.com/watch?v=MU6HCFm65aE

招生要求

The postdoctoral fellow and research assistant positions are financially supported by multiple 3- to 4-year research grants involving learning with less data as well as the 5-year DESCARTES project (https://www.cnrsatcreate.cnrs.fr/descartes/) involving hybrid AI.

For the postdoc positions, a successful candidate should have a Ph.D. in computer science, computer engineering, machine learning, statistics, math, data science, operations research or other related disciplines. A good publication record in the premier machine learning and AI conferences and/or journals is preferred. He/she must have a strong proficiency in programming.

For the RA and Ph.D. student positions, a successful candidate should have a Bachelor’s degree in computer science and engineering, statistics, math, data science, operations research or other related disciplines from a reputable university and a strong academic track record (especially in math, statistics, and algorithms courses). A good publication record in the premier machine learning and AI conferences and/or journals is a bonus. He/she must have a strong proficiency in programming.

申请方式

If you are interested to apply, please send a short cover letter describing your suitability for the position, detailed CV with academic ranking (if any) and publication list, a concise description of research interests and future plans, and academic transcripts to: Dr. Bryan Low (lowkh@comp.nus.edu.sg)

We will begin reviewing applications for the positions immediately.