The project is hiring 2 postdoctoral fellows at SUSTech (One for Statistics and Analysis of Complex Data and the other for Image Processing and Optimisation).
南方科技大学课题组现公开招聘博士后2名(统计和复杂数据分析以及图像处理和优化方向各一名).
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
Prof. Jian Qing Shi (史建清教授)
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）
史建清教授个人网页：http://faculty.sustech.edu.cn/shijq
Dr. Chao Wang （王超博士）
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, and he has made some innovative contributions to theoretical and algorithmic aspects of sparsity. In recent years, he has published papers in top journals and academic conferences such as TIP, SISC, SIIMS, ICML, IP, etc. Dr. Wang received the best paper awards in both the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) workshop in 2022 and the 15th China Society of Industrial and Applied Mathematics (CSIAM) Annual Conference in 2017.
王超，是南方科技大学统计与数据科学系的助理教授（副研究员）。他的研究方向主要是图像处理、科学计算和跨学科数据科学，并在稀疏性方面研究取得了一些创新性的理论和算法进展。近年来，他在TIP、SISC、SIIMS、ICML、IP等顶级期刊和学术会议上发表了论文。王博士分别于2022年IEEE/CVF计算机视觉与模式识别（CVPR）研讨会和2017年中国工业与应用数学学会（CSIAM）年会上获得了最佳学生论文奖。
王超副研究员个人网页https://wangcmath.github.io/
PI at NUS
Prof. Yingcun XIA (夏应存教授)
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院任助理研究员，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。
夏应存教授个人网页：XIA Yingcun (nus.edu.sg)
We are hiring 2 postdoctoral fellows. The details are listed below.
课题组现公开招聘博士后2名；具体岗位信息如下：
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, statistical models with complex structure or image processing.
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。
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。
Benefits and Rewards/待遇与福利:
1. A fixed-term contract for 2 years.
2. A competitive salary of 330,000 CNY per annum (including 150,000 CNY province-level subsidy before tax and 60,000 CNY city-level subsidy after tax. The successful candidates will receive the benefits from SUSTech including subsidy for meals, hot weather, festivals, free body check and other benefits required by the law.
3. Candidates may also apply for the chancellor’s award for outstanding postdocs, with a salary of more than 500,000 CNY per annum (including subsidy from Guangdong and Shenzhen).
4. Postdocs will receive a subsidy of 25,000 CNY from the university for academic activities during the contract term.
5. According to the specific scientific research performance of the post-doctoral students, they can enjoy the corresponding scientific research performance awards of the faculty and subject groups.
6. Successful candidates will be able to apply as the principal investigator for grants from the postdoc science foundation, the national science foundation as well as the Guangdong province and Shenzhen city.
7. Candidates graduated from the world top 200 foreign universities (according to the latest Times, USNEWS, QS and Shanghai Jiao Tong University Rankings) could apply for a living subsidy of 600,000 CNY from the Guangdong province. The successful candidates will receive a housing subsidy of 400,000 CNY after obtaining their postdoc certificates and signing a contract of more than 3 years with a company registered in Guangdong.
8. Post-doctoral students who choose to stay in Shenzhen to engage in scientific research and who have signed labor (employment) contracts with enterprises and institutions in the city for more than 3 years can apply for Shenzhen post-doctoral research grants. The Shenzhen Municipal Government gives 100,000 CNY per person per year for three years to subsidize scientific research.
9. For postdocs who meet the policy requirements of the latest ‘Shenzhen Newly Introduced Talent Rental Housing and Living Allowance’, after settling in Shenzhen, they can apply for a one-time rental housing and living allowance of 30,000 yuan in Shenzhen (tax-free, independent online application).
10. According to the conditions that they meet, post-docs who stay in Shenzhen or out of Shenzhen can apply for ‘Shenzhen Peacock Plan Class C Talents’ or ‘Shenzhen Reserve Level Talents’ and enjoy an incentive allowance of 1.6 million CNY for 5 years (tax-free) (subject to the latest declaration requirements of relevant talents in Shenzhen).
1. 博士后聘用期两年，年薪33万元起，含广东省生活补贴15万元及深圳市生活补贴6万元，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“广东省海外青年博士后引进项目”。即在世界排名前200名的高校（不含境内，排名以上一年度泰晤士、USNEWS、QS和上海交通大学的世界大学排行榜为准）获得博士学位，在广东省博士后设站单位从事博士后研究，并承诺在站2年以上的博士后，申请成功后省财政给予每名进站博士后资助60万元生活补贴（与广东省每年15万生活补贴不同时享受，与深圳市每年6万元生活补贴同时享受情况以深圳市规定为准）；对获得本项目资助，出站后与广东省用人单位签订工作协议或劳动合同，并承诺连续在粤工作3年以上的博士后，省财政给予每人40万元住房补贴。
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）。
To apply/联系方式:
To apply for the position, please send the following information to qiut@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，邮件发送至邱老师：qiut@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 Prof. Jian Qing Shi (shijq@sustech.edu.cn) or Dr. Chao Wang (wangc6@sustech.edu.cn) for an informal enquiry.
有意向者也可以预先联系史建清教授(统计和复杂数据分析shijq@sustech.edu.cn)或王超副研究员(图像处理和优化wangc6@sustech.edu.cn)。
