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一、項(xiàng)目名稱

A New Paradigm for Efficient Statistical Inference via Deep Representation Learning and Double Sampling

二、項(xiàng)目簡(jiǎn)介

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.

在大數(shù)據(jù)時(shí)代，獲取高質(zhì)量“金標(biāo)準(zhǔn)”數(shù)據(jù)（如精確臨床診斷）往往成本高昂且過(guò)程復(fù)雜，導(dǎo)致實(shí)際應(yīng)用中這類驗(yàn)證樣本的規(guī)模十分有限。與此同時(shí)，大規(guī)模、易獲取的替代數(shù)據(jù)（如常規(guī)監(jiān)測(cè)指標(biāo)）雖然包含測(cè)量誤差或信息不完整，卻蘊(yùn)含著豐富的輔助信息。如何充分利用這兩類數(shù)據(jù)實(shí)現(xiàn)高效、穩(wěn)健的統(tǒng)計(jì)推斷，是當(dāng)前統(tǒng)計(jì)學(xué)與數(shù)據(jù)科學(xué)面臨的重要挑戰(zhàn)。

本項(xiàng)目創(chuàng)新性地將深度神經(jīng)網(wǎng)絡(luò)（DNN）強(qiáng)大的表示學(xué)習(xí)能力與雙重抽樣的統(tǒng)計(jì)效率理論相結(jié)合，構(gòu)建一個(gè)源于替代數(shù)據(jù)的零均值校正項(xiàng)，并將其整合到基于驗(yàn)證樣本的參數(shù)估計(jì)中，實(shí)現(xiàn)方差縮減，從而提升估計(jì)效率，并實(shí)現(xiàn)穩(wěn)健的統(tǒng)計(jì)推斷——即推斷結(jié)果對(duì)替代數(shù)據(jù)模型具有魯棒性。該方法可廣泛應(yīng)用于醫(yī)學(xué)領(lǐng)域（如基于小樣本精確模型與大樣本替代數(shù)據(jù)的疾病診斷），以及圖像分析等其他復(fù)雜數(shù)據(jù)場(chǎng)景，為數(shù)據(jù)稀缺條件下的高效建模提供新思路。除上述具體的課題外，聯(lián)合課題組將根據(jù)申請(qǐng)人的教育背景和已有的研究基礎(chǔ)指導(dǎo)其在因果推斷、統(tǒng)計(jì)學(xué)習(xí)、AI for Statistics or Statistics for AI、AI for Finance上探索新穎的前沿課題。

三、PIs at SUSTech & CUHK

Prof. Xuejun JIANG （蔣學(xué)軍），南方科技大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)系

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). 教授簡(jiǎn)介: 蔣學(xué)軍，南方科技大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)系研究員(長(zhǎng)聘副教授), 系負(fù)責(zé)人，博士生導(dǎo)師。他于2009年博士畢業(yè)于香港中文大學(xué)統(tǒng)計(jì)系，2009-2010在港中文從事博士后研究， 2013年07月加入南方科技大學(xué)，入選深圳市海外高層次人才孔雀計(jì)劃（2016），深圳市優(yōu)秀教師（2018），主持和完成國(guó)家（廣東省）自然科學(xué)基金、深圳市基礎(chǔ)研究面上項(xiàng)目等10余項(xiàng)。其研究方向和興趣涉及復(fù)雜數(shù)據(jù)分析、特征提取、統(tǒng)計(jì)推斷，金融/應(yīng)用統(tǒng)計(jì)，機(jī)器學(xué)習(xí)(遷移學(xué)習(xí)及表征學(xué)習(xí)、輔助學(xué)習(xí)、共形推斷與預(yù)測(cè))等，已在統(tǒng)計(jì)學(xué)頂級(jí)期刊Biometrika及Bernoulli, Statistica Sinica,JBES,The Econometrics Journal, Science China-Mathematics,中國(guó)科學(xué)數(shù)學(xué)等統(tǒng)計(jì)學(xué)及計(jì)量經(jīng)濟(jì)學(xué)國(guó)內(nèi)外一流學(xué)術(shù)期刊上發(fā)表SCI&SSCI論文60余篇，授權(quán)專利2項(xiàng)及出版英文教材一部。國(guó)內(nèi)學(xué)會(huì)任職主要有中國(guó)現(xiàn)場(chǎng)統(tǒng)計(jì)研究會(huì)-教育統(tǒng)計(jì)與管理分會(huì)副理事長(zhǎng)，多元分析應(yīng)用專業(yè)委員會(huì)秘書(shū)長(zhǎng)等。Prof. Xinyuan Song (宋心遠(yuǎn))，香港中文大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)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 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.

教師簡(jiǎn)介：宋心遠(yuǎn)，香港中文大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)系教授，國(guó)際數(shù)理統(tǒng)計(jì)學(xué)會(huì)會(huì)士（IMS Fellow），國(guó)際統(tǒng)計(jì)協(xié)會(huì)當(dāng)選會(huì)員(ISI Elected Member)。她的研究興趣廣泛，包括潛變量模型、貝葉斯方法、生存分析、非參數(shù)與半?yún)?shù)方法、因果推斷及統(tǒng)計(jì)計(jì)算等。目前已在統(tǒng)計(jì)學(xué)及相關(guān)學(xué)科國(guó)際一流期刊上發(fā)表論文230余篇。宋心遠(yuǎn)教授現(xiàn)任多個(gè)國(guó)際統(tǒng)計(jì)與計(jì)量心理學(xué)期刊的副主編，包括JASA，Biometrics，Psychometrika等。

四、崗位要求

We are hiring 1 postdoctoral fellow. The details are listed below.

課題組現(xiàn)公開(kāi)招聘博士后1名，具體崗位信息如下：

01崗位要求

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

1.獲得或即將獲得統(tǒng)計(jì)學(xué)、數(shù)學(xué)、數(shù)據(jù)科學(xué)、計(jì)算機(jī)或其他相關(guān)學(xué)科的博士學(xué)位（博后的要求），境外名?；颉?85”高校相關(guān)專業(yè)博士生優(yōu)先；

2.精通R,Python/Matlab或其他至少一種計(jì)算機(jī)語(yǔ)言；

3.有較強(qiáng)的統(tǒng)計(jì)學(xué)/數(shù)學(xué)方法和理論基礎(chǔ)知識(shí)和實(shí)踐能力；有高維復(fù)雜數(shù)據(jù)分析、復(fù)雜模型或圖像處理研究經(jīng)驗(yàn)者優(yōu)先或?qū)IforStatistics,StatisticsforAI以及AI+StatisticsforPublicHealthorFinance有基礎(chǔ)和興趣的優(yōu)先；

4.具有較強(qiáng)英文寫(xiě)作能力，有論文或項(xiàng)目書(shū)等寫(xiě)作經(jīng)驗(yàn)者優(yōu)先；

5.具有良好的溝通能力和展示能力；

6.本項(xiàng)目為南方科技大學(xué)與香港中文大學(xué)課題組項(xiàng)目之間的合作，博士后在站期間可允許到香港中文大學(xué)進(jìn)行短期交流訪問(wèn)，但博士后崗位須遵循南方科技大學(xué)博士后崗位管理規(guī)定，具體合作方式面議。

02崗位職責(zé)

1. Undertake research related to the project.

2. Help to prepare research proposals.

3. Help on other research activities.

1.進(jìn)行與本課題相關(guān)的科研工作；

2.協(xié)助課題組申報(bào)各類科研課題及承擔(dān)相應(yīng)的科學(xué)研究任務(wù)；

3.協(xié)助完成課題組的其他日常工作。

03待遇與福利

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.

1.博士后聘用期兩年，綜合年薪33萬(wàn)元起（稅前，含廣東省及深圳市博士后在站生活補(bǔ)助），特別優(yōu)秀候選人可以申請(qǐng)校長(zhǎng)卓越博士后，年薪可達(dá)50萬(wàn)元以上（含省市補(bǔ)助）。

2.廣東省對(duì)符合條件的在站博士后發(fā)放每人總額30萬(wàn)元（稅前）的生活補(bǔ)助，深圳市對(duì)符合條件的在站博士后發(fā)放每人總額12萬(wàn)元（稅前）的生活補(bǔ)助，資助期為24個(gè)月。

3.在站期間，可依托學(xué)校申請(qǐng)深圳市公租房，未依托學(xué)校使用深圳市公租房的博士后，可享受兩年稅前2800元/月的住房補(bǔ)貼。

4.擁有優(yōu)良的工作環(huán)境和境內(nèi)外合作交流機(jī)會(huì)，博士后在站期間享受兩年共計(jì)2.5萬(wàn)學(xué)術(shù)交流經(jīng)費(fèi)資助。

5.課題組可協(xié)助符合條件的博士后申請(qǐng)博士后人才項(xiàng)目。獲批最高可享受總計(jì)100萬(wàn)元補(bǔ)貼（與省市補(bǔ)助不同時(shí)享受）。

6.博士后出站后6個(gè)月內(nèi)留（來(lái)）深全職工作且與企事業(yè)單位簽訂3年以上勞動(dòng)（聘用）合同的，深圳市政府給予每人36萬(wàn)元出站來(lái)深生活補(bǔ)助。

7.在站期間獲得博士后創(chuàng)新人才支持計(jì)劃或中國(guó)博士后科學(xué)基金特別資助的優(yōu)秀博士后人員，且出站后6個(gè)月內(nèi)與本市簽訂3年以上勞動(dòng)（聘用）合同的，深圳市政府再按照國(guó)家資助標(biāo)準(zhǔn)給予1：1經(jīng)費(fèi)資助，最高不超過(guò)30萬(wàn)元。

8.獲得全國(guó)或廣東省博士后創(chuàng)新創(chuàng)業(yè)大賽金獎(jiǎng)、銀獎(jiǎng)、銅獎(jiǎng)的，且出站后6個(gè)月內(nèi)與本市簽訂3年以上勞動(dòng)（聘用）合同的，深圳市政府再按照國(guó)家和省獎(jiǎng)勵(lì)金額給予1：1創(chuàng)新創(chuàng)業(yè)獎(jiǎng)勵(lì)，最高不超過(guò)20萬(wàn)元。

9.根據(jù)《深圳市博士后工作管理規(guī)定》第三十九條規(guī)定，該規(guī)定資助項(xiàng)目與深圳市新引進(jìn)博士人才生活補(bǔ)貼（10萬(wàn)元）不重復(fù)享受。

04聯(lián)系方式（before31Aug,2026）

To apply for the position, please send the following information to Prof. Jiang（（點(diǎn)擊查看））and Prof. Song（（點(diǎn)擊查看）） 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.有意向者請(qǐng)將個(gè)人詳細(xì)簡(jiǎn)歷(包括成績(jī)單和已發(fā)表文章的完整列表)、代表性學(xué)術(shù)成果等整合為一個(gè)PDF文件，郵件發(fā)送至蔣學(xué)軍老師（（點(diǎn)擊查看） ）和宋心遠(yuǎn)老師（（點(diǎn)擊查看））郵件標(biāo)題請(qǐng)注明：SUSTech & CUHK聯(lián)合研究項(xiàng)目-崗位-姓名-專業(yè)。

信息來(lái)源于網(wǎng)絡(luò)，如有變更請(qǐng)以原發(fā)布者為準(zhǔn)。

來(lái)源鏈接：

https://mp.weixin.qq.com/s/0NAJKllmIjp3-02K7nj-4g

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