Title/项目名称：A New Paradigm for Efficient Statistical Inference via Deep Representation Learning and Double Sampling。
Abstract：In the era of big data, obtaining high-quality "gold standard" data (e.g., precise clinical diagnoses) is often costly and procedurally complex, resulting in very limited sample sizes for such validation data in practice. Meanwhile, large-scale and easily accessible surrogate data (e.g., routine monitoring indicators), despite containing measurement errors or incomplete information, are rich in auxiliary information. How to efficiently and robustly leverage both types of data for statistical inference poses a significant challenge in contemporary statistics and data science.
This project innovatively integrates the powerful representation learning capability of deep neural networks (DNNs) with the statistical efficiency theory of double sampling. We construct a zero-mean correction term derived from the surrogate data and incorporate it into the parameter estimation based on the validation sample. This approach achieves variance reduction, thereby improving estimation efficiency, and enables robust statistical inference—meaning that the inference results are robust to the surrogate data model. This method can be widely applied in medicine (e.g., disease diagnosis using small-sample accurate models combined with large-sample surrogate data) as well as in other complex data scenarios such as image analysis, offering a new pathway for efficient modeling under data scarcity.
Beyond the specific project described above, the joint research team will guide the applicant in exploring novel and cutting-edge topics in causal inference, statistical learning, AI for Statistics or Statistics for AI, and AI for Finance based on the applicant's educational background and existing research foundation.
项目简介：在大数据时代，获取高质量“金标准”数据（如精确临床诊断）往往成本高昂且过程复杂，导致实际应用中这类验证样本的规模十分有限。与此同时，大规模、易获取的替代数据（如常规监测指标）虽然包含测量误差或信息不完整，却蕴含着丰富的辅助信息。如何充分利用这两类数据实现高效、稳健的统计推断，是当前统计学与数据科学面临的重要挑战。
本项目创新性地将深度神经网络（DNN）强大的表示学习能力与双重抽样的统计效率理论相结合，构建一个源于替代数据的零均值校正项，并将其整合到基于验证样本的参数估计中，实现方差缩减，从而提升估计效率，并实现稳健的统计推断——即推断结果对替代数据模型具有鲁棒性。该方法可广泛应用于医学领域（如基于小样本精确模型与大样本替代数据的疾病诊断），以及图像分析等其他复杂数据场景，为数据稀缺条件下的高效建模提供新思路。
除上述具体的课题外，联合课题组将根据申请人的教育背景和已有的研究基础指导其在因果推断、统计学习、AI for Statistics or Statistics for AI、AI for Finance上探索新颖的前沿课题。
教师简介：
1. Prof. Xuejun JIANG （蒋学军），南方科技大学统计与数据科学系
Research Group Website: https://faculty.sustech.edu.cn/jiangxj/
Faculty Profile: Professor Xuejun Jiang is a tenured Associate Professor, Deputy Department Head, and doctoral supervisor at the Department of Statistics and Data Science, Southern University of Science and Technology (SUSTech). He earned his Ph.D. in Statistics from The Chinese University of Hong Kong (CUHK) in 2009, followed by postdoctoral research there (2009–2010), and joined SUSTech in 2013. He was honored as a recipient of the Shenzhen Overseas High-Level Talent Peacock Program (2016) and a Shenzhen Outstanding Teacher (2018). He has led over 10 research projects funded by the NSFC, Guangdong Provincial Natural Science Foundation, and Shenzhen Basic Research Program, etc. Xuejun Jiang’s research interests include complex data analysis, feature extraction, statistical inference, financial and applied statistics, and machine learning (e.g., transfer learning, representation learning, auxiliary learning, conformal inference, etc.). He has published over 60 SCI/SSCI papers in leading journals including Biometrika, Bernoulli, Statistica Sinica, JBES, The Econometrics Journal, Science China-Mathematics, and Scientia Sinica-Mathematica with two authorized patents and one English textbook.
Professor Jiang also serves as Vice Chairperson of the Educational Statistics and Management Branch and Secretary-General of the Multivariate Analysis Application Committee under the Chinese Association for Applied Statistics (CAAS).
教授简介: 蒋学军，南方科技大学统计与数据科学系研究员(长聘副教授), 系负责人，博士生导师。他于2009年博士毕业于香港中文大学统计系，2009-2010在港中文从事博士后研究， 2013年07月加入南方科技大学，入选深圳市海外高层次人才孔雀计划（2016），深圳市优秀教师（2018），主持和完成国家（广东省）自然科学基金、深圳市基础研究面上项目等10余项。其研究方向和兴趣涉及复杂数据分析、特征提取、统计推断，金融/应用统计，机器学习(迁移学习及表征学习、辅助学习、共形推断与预测)等，已在统计学顶级期刊Biometrika及Bernoulli, Statistica Sinica,JBES,The Econometrics Journal, Science China-Mathematics,中国科学数学等统计学及计量经济学国内外一流学术期刊上发表SCI&SSCI论文60余篇，授权专利2项及出版英文教材一部。国内学会任职主要有中国现场统计研究会-教育统计与管理分会副理事长，多元分析应用专业委员会秘书长等。
2. Prof. Xinyuan Song (宋心远)，香港中文大学统计与数据科学系
Research Group Website: http://www.sta.cuhk.edu.hk/xysong/
Faculty Profile: Song Xinyuan is a Professor in the Department of Statistics and Data Science at The Chinese University of Hong Kong (CUHK), a Changjiang Chair Professor appointed by the Ministry of Education of China, a Fellow of the Institute of Mathematical Statistics (IMS Fellow), as well as an Elected Member of the International Statistical Institute (ISI). Her research interests span a broad range of areas, including include latent variable models, Bayesian methods, survival analysis, nonparametric and semiparametric methods, causal inference, and statistical computing, etc. She has published over 230 papers in leading international journals in statistics and related fields. Additionally, Professor Song serves as an Associate Editor for several top-tier international journals in statistics and psychometrics, such as JASA (Journal of the American Statistical Association), Biometrics, and Psychometrika, etc.
教师简介：宋心远，香港中文大学统计与数据科学系教授，教育部长江学者讲座教授，国际数理统计学会会士（IMS Fellow），国际统计协会当选会员(ISI Elected Member)。她的研究興趣广泛，包括潜变量模型、贝叶斯方法、生存分析、非参数与半参数方法、因果推断及统计计算等。目前已在统计学及相关学科国际一流期刊上发表论文230余篇。宋心远教授现任多个国际统计与计量心理学期刊的副主編，包括JASA，Biometrics，Psychometrika等。
Job Requirements/岗位要求
We are hiring 1 postdoctoral fellow. The details are listed below.
课题组现公开招聘博士后1名；具体岗位信息如下：
01 Job Requirements/岗位要求
For Postdoc:1. Hold a PhD degree (or complete a PhD program in 2026) in Statistics, Mathematics, Data Science, Computer Science or other related areas. Graduates from renowned overseas universities or "985" universities in China are preferred. 2. Proficiency in R,Python/Matlab or other computer languages.3. Good knowledge and strong research abilities in statistical/mathematical methodology, theory and implementation, preferable on high-dimensional data analysis, complex modeling, or image processing, as well as those who have a foundational understanding and interest in AI for Statistics, Statistics for AI, or AI+Statistics for Public Health or Finance. 4. Excellent English writing skills are required. Prior experience in writing research papers or grant proposals is preferred. 5. Good communication and presentation skills in both English and Chinese. 6.This project is a collaboration between the research group projects of Southern University of Science and Technology and The Chinese University of Hong Kong (CUHK). During the postdoctoral period, candidates may have the opportunity to conduct short-term visits and exchanges at CUHK.The postdoctoral position must comply with the postdoctoral position management regulations of Southern University of Science and Technology. Specific cooperation details are to be discussed in person.
博后方面：1. 获得或即将获得统计学、数学、数据科学、计算机或其他相关学科的博士学位（博后的要求），境外名校或“985”高校相关专业博士生优先；2. 精通R,Python/Matlab或其他至少一种计算机语言；3. 有较强的统计学/数学方法和理论基础知识和实践能力；有高维复杂数据分析、复杂模型或图像处理研究经验者优先或对AI for Statistics,Statistics for AI以及AI+Statistics for Public Health or Finance有基础和兴趣的优先4. 具有较强英文写作能力，有论文或项目书等写作经验者优先；5. 具有良好的沟通能力和展示能力。6.本项目为南方科技大学与香港中文大学课题组项目之间的合作，博士后在站期间可允许到香港中文大学进行短期交流访问，但博士后岗位须遵循南方科技大学博士后岗位管理规定，具体合作方式面议。
02 Duties and Responsibilities/岗位职责
1. Undertake research related to the project.
2. Help to prepare research proposals.
3. Help on other research activities.
1. 进行与本课题相关的科研工作；
2. 协助课题组申报各类科研课题及承担相应的科学研究任务；
3. 协助完成课题组的其他日常工作。
03 Benefits and Rewards/待遇与福利
For Postdoc:
1. The postdoctoral employment period is two years, with a comprehensive annual salary starting from 330,000 RMB (before tax, including living subsidies for postdocs in station from Guangdong Province and Shenzhen City). Exceptionally outstanding candidates can apply for the President's Distinguished Postdoctoral Fellowship, with an annual salary of up to 500,000 RMB or more (including provincial and municipal subsidies).
2. Guangdong Province provides a total living subsidy of 300,000 RMB (before tax) per person for eligible postdocs in station. Shenzhen City provides a total living subsidy of 120,000 RMB (before tax) per person for eligible postdocs in station, with a funding period of 24 months.
3. During the station period, postdocs can rely on the school to apply for Shenzhen public rental housing. Postdocs who do not use Shenzhen public rental housing through the school can enjoy a pre-tax housing subsidy of 2,800 RMB/month for two years.
4. Possess an excellent working environment and opportunities for domestic and international cooperative exchange. Postdocs enjoy a total of 25,000 RMB in academic exchange funding during their two-year station period.
5. The research group can assist eligible postdocs in applying for postdoctoral talent projects. Upon approval, a maximum total subsidy of 1 million RMB can be enjoyed (cannot be enjoyed simultaneously with provincial and municipal subsidies).
6. For postdocs who stay in (or come to) Shenzhen for full-time work within 6 months after leaving the station and sign a labor (employment) contract of 3 years or more with enterprises or public institutions, the Shenzhen Municipal Government will provide a living subsidy of 360,000 RMB per person for coming to Shenzhen after leaving the station.
7. Outstanding postdoctoral personnel who obtain the Postdoctoral Innovative Talent Support Program or the special funding from the China Postdoctoral Science Foundation during the station period, and sign a labor (employment) contract of 3 years or more with this city within 6 months after leaving the station, the Shenzhen Municipal Government will provide 1:1 matching funds according to national funding standards, up to a maximum of 300,000 RMB.
8. For those who win the Gold, Silver, or Bronze awards in the National or Guangdong Postdoctoral Innovation and Entrepreneurship Competition, and sign a labor (employment) contract of 3 years or more with this city within 6 months after leaving the station, the Shenzhen Municipal Government will provide 1:1 matching innovation and entrepreneurship rewards according to the national and provincial reward amounts, up to a maximum of 200,000 RMB.
9. According to Article 39 of the "Regulations on the Administration of Postdoctoral Work in Shenzhen", the funding items in these regulations and the living subsidy for newly introduced postdoctoral talents in Shenzhen (100,000 RMB) shall not be enjoyed repeatedly.
博后方面：
1.博士后聘用期两年，综合年薪33万元起（税前，含广东省及深圳市博士后在站生活补助），特别优秀候选人可以申请校长卓越博士后，年薪可达50万元以上（含省市补助）。
2.广东省对符合条件的在站博士后发放每人总额30万元（税前）的生活补助，深圳市对符合条件的在站博士后发放每人总额12万元（税前）的生活补助，资助期为24个月。
3.在站期间，可依托学校申请深圳市公租房，未依托学校使用深圳市公租房的博士后，可享受两年税前2800元/月的住房补贴。
4.拥有优良的工作环境和境内外合作交流机会，博士后在站期间享受两年共计2.5万学术交流经费资助。
5.课题组可协助符合条件的博士后申请博士后人才项目。获批最高可享受总计100万元补贴（与省市补助不同时享受）。
6.博士后出站后6个月内留（来）深全职工作且与企事业单位签订3年以上劳动（聘用）合同的，深圳市政府给予每人36万元出站来深生活补助。
7.在站期间获得博士后创新人才支持计划或中国博士后科学基金特别资助的优秀博士后人员，且出站后6个月内与本市签订3年以上劳动（聘用）合同的，深圳市政府再按照国家资助标准给予1：1经费资助，最高不超过30万元。
8.获得全国或广东省博士后创新创业大赛金奖、银奖、铜奖的，且出站后6个月内与本市签订3年以上劳动（聘用）合同的，深圳市政府再按照国家和省奖励金额给予1：1创新创业奖励，最高不超过20万元。
9.根据《深圳市博士后工作管理规定》第三十九条规定，该规定资助项目与深圳市新引进博士人才生活补贴（10万元）不重复享受。
04 To apply/联系方式 (本招聘截止到2026年08月31日)
To apply for the position, please send the following information to Prof. Jiang（jiangxj@sustech.edu.cn）and Prof. Song（xysong@sta.cuhk.edu.hk） with the title “SUSTECH & CUHK JOINT RESEARCH PROJECT -position-your name-your major”.1.Resume (with a complete list of publications and transcripts).2. The full manuscript of 2 representative publications. 3. Other research outputs such as books, patents, etc.有意向者请将个人详细简历(包括成绩单和已发表文章的完整列表)、代表性学术成果等整合为一个PDF文件，邮件发送至蒋学军老师（jiangxj@sustech.edu.cn ）和宋心远老师（xysong@sta.cuhk.edu.hk）邮件标题请注明：SUSTech & CUHK联合研究项目-岗位-姓名-专业。
