The project is hiring 1 postdoctoral fellows at SUSTech for Image Processing and Deep Learning).南方科技大学课题组现公开招聘博士后1名(图像处理和深度学习方向)。

Title/项目名称

Tensor Dimension Reduction for Multimodal Data and its Application in EEG Analysis（多模态张量降维方法研究和在EEG数据分析中的应用）

Abstract/项目简介

Multimodal data analysis, driven by the increasing variety of data types in applications, has attracted much interest in statistics, focusing on encoding different modalities into a common representation space to build predictive models or explore relationships between modalities.The main goal of the project is to build models to describe complicated dependencies between modalities.This is particularly challenging when at least one modality is a tensor due to the complexity of tensor structures.Our idea is to introduce a novel Structural Equation Model (SEM) based tensor matrix factorisation model to simultaneously extract latent scores and build relationships between modalities.The context of the proposal is the study of insomnia patients, where EEG data and questionnaire responses are analysed to understand the relationship between these very different types of data.The project outlines four specific problems, including the development of new tensor matrix factorisation models, the investigation of identifiability conditions, the measurement of non-linear dependence in tensor decomposition, and the study of functional connectivity models in the context of tensor matrix factorisation.statistical inference, implementation tools, optimisation algorithms and theory of both statistical and optimisation errors will be studied.Applications to the analysis of other wearable and mobile data will also be explored.The project will include but is not limited to the following four topics (1) SEM-based tensor matrix factorization model; (2) Nonlinear tensor matrix factorisation models and identifiability; (3) Tensor Decomposition with a measure of nonlinear dependence; (4) Functional connectivity analysis based on factorisation model.

由于在实际应用中的数据类型日益多样化，多模态数据分析引起了统计学界的极大兴趣。其核心在于将不同模态的数据编码到一个共同的空间中，以建立预测模型或探索各模态之间的关系。该项目的主要目标是建立模型来描述模态之间潜在的依赖关系。由于涉及张量结构的复杂性，当至少一种模态为张量时，这项工作尤其具有挑战性。我们的思路是引入一种新颖的基于结构方程模型的张量矩阵因式分解模型，以同时提取潜在变量并建立模态之间的关系。该项目的背景是针对失眠症患者的研究，通过分析脑电图数据和问卷答复来了解这些截然不同数据类型之间的关系。该项目包含了四个具体问题，包括开发新的张量矩阵因式分解模型、研究可识别性条件、测量张量分解中的非线性依赖性以及在张量矩阵因式分解背景下研究功能连接模型。该项目研究范围涵盖了统计推断、实现工具、优化算法以及统计和优化误差理论。此外，还将探讨该方法在其他可穿戴和移动数据分析方面的应用潜力。该项目将包括但不限于以下四个主题：(1)基于结构方程模型的张量矩阵因式分解模型；(2)非线性张量矩阵因式分解模型与可识别性；(3)考虑非线性依赖的张量分解；(4)基于因式分解模型的功能连接分析。

PIs at SUSTech

Dr.Chao Wang （王超博士）

个人主页：https://wangcmath.github.io/

Dr.Wang is an Assistant Professor of the Department of Statistics and Data Science at Southern University of Science and Technology.His research directions are mainly image processing, scientific computing, and interdisciplinary data science.He has published over 30 papers in top-tier journals such as the SIAM series and IEEE Transactions, as well as leading conferences, and has received notable accolades including the Best Paper Award at the 2022 CVPR Workshop, the Shenzhen Pengcheng Peacock Plan Distinguished Professorship (2021), and the Best Paper Award at the 2017 Annual Meeting of China Society for Industrial and Applied Mathematics.He has led research projects such as the National Natural Science Foundation of China Youth Program, Guangdong Basic and Applied Research Foundation Program, and Shenzhen Science and Technology Program, while also contributing as a principal investigator or core member to major initiatives including the National Key Research and Development Program, Hong Kong RGC Research Fund projects, and Shenzhen Fundamental Research Program.王超，南方科技大学统计与数据科学系副研究员，博导，其研究方向主要为图像处理、科学计算与交叉学科的数据科学。在本领域期刊SIAM系列、IEEE汇刊等杂志及学术会议发表学术论文三十余篇。在2022年CVPR研讨会获得最佳论文，在2021年获深圳市鹏城孔雀计划特聘岗位，在2017年获得中国工业与应用数学学会年会最佳论文。主持国自然青年基金、广东省面上基金以及深圳市稳定支持面上项目，以课题负责人或核心成员参与国家重点研发项目、香港研资局科研基金项目以及深圳重点项目。

Prof.Jian Qing Shi (史建清教授)

个人主页：http://faculty.sustech.edu.cn/shijq

Dr Jian Qing Shi is a Professor of Statistics in the Department of Statistics and Data Science and the director of the Center for Biostatistics at Southern University of Science and Technology (SUSTech), China.Before joining SUSTech in 2020, he was Turing Fellow in the Alan Turing Institute UK and assistant director of the Cloud Computing for Big Data CDT in Newcastle University UK.His research interests include functional data analysis, Bayesian nonparametric analysis for big data, missing data, meta-analysis and applications in medicine and system control.He is the principal investigator of several big research projects in both China and UK, including a recently awarded grant of National Key R&D Program of China (PI, 11.1 million RMB).史建清，南方科技大学统计与数据科学系教授，理学院生物医学统计中心主任，英国皇家统计学会会士。2020年加入南科大前曾任英国国家艾伦图灵研究院图灵研究员，剑桥大学牛顿学院访问研究员，英国纽卡斯尔大学（Newcastle University）统计学教授，纽卡斯尔大学云计算和大数据研究中心副主任。主要研究方向包括函数型数据分析，生物医学统计，缺失数据分析，meta-analysis等。在国际学术刊物上发表高水平学术论文多篇，包括统计和医学顶级期刊 JRSSB, JASA, Biometrika, Nature Medicine 和British Medical Journal。获得多个国际奖项和多项国际国内项目资助包括2023年获得的科技部十四五重点项目（主持，共一千一百多万）。

PIs at NUS

Prof.Yingcun XIA (夏应存教授)

个人主页：https://blog.nus.edu.sg/homepage/research/

Dr Yingcun XIA is a Professor of Statistics in the Department of Statistics and Data Science at the National University of Singapore (NUS).He worked as a Research Associate at the London School of Economics and Political Science and at Cambridge University before he joined NUS in 2003.He is the AE of several journals, including Annals of Statistics and Computational Statistics & Data Analysis.His recent research interests include measure of nonlinear dependence, dimension reduction, financial time series, and environment statistics.He has published papers in journals including PNAS， AoS， JASA， JRSSB， American Naturist, and JoE.Several papers have been selected as discussion papers by JRSSB， Statistical Sciences, and Statistica Sinica and were reported by Nature News and other media.

夏应存，新加坡国立大学统计与应用概率系教授， 2000年至2001年在伦敦政治经济学任助理研究员，2001年至2003 年为剑桥大学助理研究员。现任Annals of Statistics, Computational Statistics & Data Analysis等期刊的副主编。夏应存的研究兴趣包括非参数回归分析，计量经济及金融时间序列分析，高维数据分析，环境与健康的统计分析。在学术期刊PNAS， AoS， JASA， JRSSB， American Naturist， JoE 等发表多篇论文。部分论文在JRSSB，Statistical Sciences及Statistica Sinica等期刊上专题讨论；Nature News等十几家学术媒体对其工作做过专题报道。

Postdoc Positions/博士后岗位招聘

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

1.Hold a PhD degree (or complete a PhD program in 2024) in Statistics, Mathematics, Computer Science or other related areas.

2.Proficiency in Python/R or other computer languages.

3.Good knowledge and strong research abilities in statistical/mathematical methodology, theory and implementation, preferable on high-dimensional data analysis, image processing or deep learning.

4.Preference of having experience in preparing research papers or proposals in English.

5.Good communication and presentation skills in both English and Chinese.

1.获得或即将获得统计、数学、计算机或其他相关学科的博士学位，境外名校或“985”高校相关专业博士生优先；

2.精通Python/R或其他至少一种计算机语言；

3.有较强的统计/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；

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。

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

1.博士后聘用期两年，年薪33万元起，含广东省生活补贴15万元（税前）及深圳市生活补贴6万元（税前），并按深圳市有关规定参加社会保险及住房公积金。博士后福利费参照学校教职工标准发放。

2.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。（含广东省及深圳市在站生活补贴）。

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.课题组协助符合条件的博士后申请博士后人才项目。如成功申请，最高可享受总计100万元补贴（与广东省及深圳市在站博士后生活补贴不同时享受）。

6.博士后出站选择留深从事科研工作，且与本市企事业单位签订3年以上劳动（聘用）合同的，可以申请深圳市博士后留深来深科研资助。深圳市政府给予每人每年10万元科研资助，共资助3年（以深圳市最新申报要求为准）。

7.根据《深圳市新引进博士人才生活补贴工作实施办法》规定，新引进博士人才生活补贴（10万元）与省市博士后在站生活补贴不同时享受。

04、To apply/联系方式

To apply for the position, please send the following information to lihw3@mail.sustech.edu.cn with the title “SUSTECH&NUS JOINT RESEARCH PROJECT -postdoc-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，邮件发送至李老师：lihw3@mail.sustech.edu.cn。

邮件标题请注明：SUSTech&NUS联合研究项目-博士后岗位-姓名-专业。

You may contact Dr.Chao Wang (wangc6@sustech.edu.cn) for an informal enquiry.

有意向者也可以预先联系王超副研究员(wangc6@sustech.edu.cn)。

Introduction of the Department of Statistics and Data Science at SUSTech/南方科技大学统计与数据科学系介绍

SUSTech

Established in April 2019, the Department of Statistics and Data Science aims to institute a world-class learning and research center.The department is committed to cultivating top-notch talents’ proactive thoughts, innovative thinking, global vision, as well as their core skills in scientific methodologies.The department now offers two undergraduate programs: major in Statistics, and major in Data Science and Big Data Technology.The department also offers M.Sc.and Ph.D.programs in Probability and Statistics.Key specialized research areas of our faculty members include Mathematical Statistics, Biostatistics, Financial Statistics and Data Science.

We currently have 22 full-time and 4 jointly appointed faculty members (including 3 new members who will join the department soon), comprising 4 Chair Professors, 4 Professors, 7 Associate Professors and 11 Assistant Professors.All faculty members of the department have extensive overseas study or working experiences, including an invited speaker at the International Congress of Mathematicians, two winners of the prestigious State Natural Science Award (2nd class), two Institute of Mathematical Statistics (IMS) Fellows, an IMS Council member, a Fellow of American Statistical Association, an IMS Medallion Lecturer, a Fellow of the Royal Statistics Society, a Fellow of the British Computer Society.

Thanks to the rich and quality resources of SUSTech,we have established the center for biostatistics, the center for data science and the statistic consulting center.The center for financial statistics will also be in place in the future.By working with SUSTech Global, we provide high-quality and multi-type International Education Training and Development Plans for undergraduates and postgraduates, as well as the opportunities to study in overseas universities.We are committed to enhancing student’s social vision and well-rounded thinking.

南方科技大学统计与数据科学系成立于2019年4月，以建设国际一流的教育培养和研究基地为目的。本系志在为国家培养出具有扎实的科学基础，思想活跃，创新意识和能力强，有国际视野，脚踏实地，有朝气、有理想的拔尖人才。本系已经建立起本硕博人才培养体系，拥有统计学和数据科学与大数据技术两个本科专业，及数学学科下概率论和数理统计硕博学位授予权，主要研究领域包括数理统计、生物医学统计、金融统计和数据科学。

目前共有22位教研序列教师和4位双聘教师，其中有讲席教授4人，教授4人，副教授7人，助理教授11人。本系拥有国际化、高水平的师资队伍，包括1名国际数学家大会邀请报告人，2名国家自然科学奖二等奖获得者，1名长江讲座教授，2名国际数理统计学会（IMS）会士，1名IMS常务理事，1名美国统计学会（ASA）会士，1名IMS Medallion讲座演讲者，1名英国皇家统计学会会士、1名英国计算机学会会士，1名广东省特支科技创新青年拔尖人才，1名深圳市杰出人才培养对象，1名深圳市高层次国家级领军人才和2名深圳市优秀教师。

本系充分利用学校及学院的优势，汇聚优质教学资源。目前已建立起生物医学统计中心和统计咨询中心，未来将会建立金融统计中心、数据科学中心和高性能服务器房，为学生提供了良好的学习环境和科研平台。同时，南科大国际合作部和统计与数据科学系为本科生及研究生提供高水平、多类型的“国际化教育培养拓展计划”，包括多个国内外知名大学的暑期或学期交换机会，旨在加强学生专业知识学习的基础上拓展学生的社会视野与社会阅历，培育学生多元化思维模式。

Introduction of the Department of Statistics and Data Science at NUS/

新加坡国立大学统计与数据科学系介绍

The Department of Statistics and Applied Probability (DSAP) was established on 1 April 1998 and renamed to the Department of Statistics and Data Science (DSDS) on 1 July 2021 with the goals of advancing statistical and data science and, ultimately, by its application to improve and provide adequate services to our community.The department offers two Bachelor of Science degrees: Statistics, Data Science, and Analytics.In addition, DSDS also offers degrees at both Masters and doctoral levels.

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原文出处：

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