语甜扯 发表于 2019-12-27 14:48:00

加拿大西安大略大学招收机器学习方向博士生 (全奖\CSC\联培&访学)


本人是加拿大西安大略大学(the university of western ontario, uwo 现更名为western university) 计算机系助理教授,同时为加拿大著名的人工智能研究中心vector institute的associate member。现热诚欢迎有志于在人工智能机器学习领域做拓展性研究的同学加入我的团队。
实验室初期的研究将集中在(但不限于)两个方面:(1)机器学习,尤其是迁移学习,多任务学习,终身学习,以及元学习的理论和算法研究;(2)机器学习算法在大脑信号处理方面的应用。

以下为一些具体要求(csc学生可适当放宽要求):
preferred candidate:
•      meet the minimum admission requirements
•      bachelor and/or master in machine learning, computer science, mathematics, statistics, physics, neuroscience, biomedical/electrical engineering, or other related quantitative fields (from the top 20 universities in china, minimum gpa: top 20% or 85/100).
•      good programming skills (e.g., experience with pytorch/tensorflow)
•      good english communication and writing skills (gre is not required)
•      solid background in math (e.g., probability, optimization)
•      self-motivated
•      a strong publication record in top-tier conferences/journals is a big plus.

感兴趣的博士申请者请联系dr. boyu wang,以"姓名+博士申请"为tile将cv发送至bwang@csd.uwo.ca。我们也同时欢迎self-funded的博士后以及访问学者加入及合作。对于符合背景的申请者,我们会尽快和您联系。

关于uwo和pi:
西安大略大学是位于加拿大安大略省伦敦市的一所世界著名学府,加拿大顶尖大学之一。长期以来,稳居加拿大top10,世界top200。学校历史悠久,是加拿大old four成员之一 (另外三所为麦吉尔大学,多伦多大学,以及女皇大学),并且拥有全加拿大最美丽的校园。
本人于在mcgill university获得博士学位,师从知名人工智能专家joelle pineau (aaai fellow, cifar senior fellow, 曾担任imls的president,icml的program chair以及genereal chair)。之后分别在princeton university以及university of pennsylvania担任博士后研究员。我们的研究立足于将人工智能与人类智能相结合,探索智能最本质的问题,并将其应用于实际问题当中。近年来,我们的工作持续发表在最顶级的ai和ml的会议(e.g., neurips, aistats, aaai, ijcai)以及相关的应用领域期刊中(e.g., ieee tbme, tsg, tkde,journal of neuroscience, journal of neural engineering)。于此同时,我们与多所世界顶尖大学 (e.g., 宾夕法尼亚大学,普林斯顿大学,哈佛大学,哥伦比亚大学,麦吉尔大学,多伦多大学,东京大学) 在机器学习,神经科学,生物医学工程等领域有着广泛的合作,也将为实验室成员提供访问交流的机会。

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