博后招募 | 新加坡国立大学WING实验室招募自然语言处理方向博士后
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WING is a research group consisting of 15-20 full-time postgraduate and undergraduate students working primarily in the areas of informational retrieval, natural language processing and digital libraries, led by Associate Professor Min-Yen Kan. It is one of several groups at the nexus of applied AI research in Singapore. The School of Computing at the National University of Singapore is one of Asia’s largest and top-ranked Computer Science and Information Systems departments, featuring a faculty expertise spanning both wide and deep areas of interests.实验室主页：
http://wing.comp.nus.edu.sg/Job Description
A postdoctoral fellow position is available in the Department of Computer Science at the National University of Singapore’s School of Computing, starting immediately. For this role, our Web, IR / NLP Group (WING) is primarily interested in developing and applying current deep learning approaches to scholarly documents to extract, visualize, recommend and summarize scholarly documents (such as journal articles, conference submissions and pre-prints).
The research program at the School of Computing at NUS has strong expertise and track record in Natural Language Processing, Information Retrieval and Digital Libraries research, where state-of-the-art approaches to current neural-based NLP are integrated with corpus analysis to achieve the next generation of scholarly communication research.
The post-doc will join a vibrant team of researchers and receive close mentorship for developing an independent teaching or research career as well as team science. We are recruiting for a research programme that will create widely applicable scholarly processing tools to help discipline-specific scholars better utilize, understand, predict and summarize the literature in their respective fields.
These projects involve large-scale data resources, computational infrastructure and open source tools and open access publications. There is ample project flexibility depending on applicant interests. We seek to publish in computer science conferences as well as to develop toolkits that general scholars beyond the domain of computer science can apply on scholarly works of their own domain’s interests. There are also opportunities for applicants to lead projects and to mentor Ph.D., MS, and talented undergraduate students and exceptional international interns.
导师简介:
Min-Yen Kan（靳民彦），新加坡国立大学副教授、计算机学院院长助理，ACM、IEEE 高级会员。他的研究兴趣包括自然语言处理、数字图书馆和信息检索。曾担任 ACL Anthology 主编、ACL 协会执行委员会成员、ACL 协会首任信息官，曾在 JCDL 2018、ACL 2017、BIRNDL 2016、AIRS 2010 等学术会议任程序委员会联合主席。获 CIKM 2020 和 JCDL 2013 最佳论文奖，2019 年 ACL 杰出服务奖。
学术主页：
https://www.comp.nus.edu.sg/~kanmy/
Qualifications
Applicants must have trained with a strong emphasis on machine learning and text mining using Python and solid understanding of corpus analysis and deep learning. Preference will be given to individuals with expertise in big data/modeling and those with an interest in scholarly communication. The position is open to graduating Ph.D., M.D. or M.D./Ph.D. students in computer science, informatics, electrical engineering or a related discipline. Current postdoctoral fellows with less than three years of postdoctoral experience are also welcomed. Applicants must be able to legally work in Singapore. Due to COVID-19, applicants may also negotiate a remote work option, if border controls do not permit relocation to Singapore. Candidates from self-identifying minorities are also especially welcomed to apply.
Appointments are initially for one year. The appointment will be offered on a definite contract basis of one year, which can be extended one or two additional years depending on available funds and performance, subject to a probationary period of six months. Salary is commensurate with research experience and education.
申请方式:
Applicants should submit their letter of application together with their curriculum vitae, certificates and names and e-mails of three referees by e-mail to Associate Professor Min-Yen Kan kanmy@comp.nus.edu.sg. We regret to say that only shortlisted applicants will be contacted.
