The research group of Han Dingding from the School of Information Science and Engineering is recruiting a postdoctoral fellow.

Introduction to the Super Complex Network Science and Intelligent System Laboratory

The Super Complex Network Science and Intelligent System Laboratory, along with the Fudan-Boya Joint Laboratory for Intelligent Information Processing and the Fudan-Zhi Xiang Information Technology Development Joint Laboratory, are established based on relevant research institutions and enterprises, including the School of Information Science and Engineering of Fudan University.

Our main research directions encompass:

• Super complex network models and big data analysis.
• Super complex network dynamics and its applications.
• Cross-disciplinary research on super complex network dynamics and machine learning.
• Key technologies and demonstration of AI + Smart City.

Focusing on real-time intelligent service systems for aerospace information, information-physical integration systems (CPS), intelligent Internet of Things (AIoT), and theoretical and application issues of super complex network communication, our team conducts diverse research. This includes:

• The correlation between time-varying characteristics of space networks and network topologies.
• The evolution and modeling of multi-dimensional and multi-layer complex networks.
• The fluctuation dynamics of complex CPS systems.
• The seepage of super complex networks and the security performance of distributed systems under time-varying conditions.
• Search and navigation in distributed systems.
• The development and application of visualization platforms for the physical-logical framework of smart cities.

We have undertaken research for several major national research projects, major natural science foundation projects, and key projects. This has allowed us to accumulate rich real-world case studies and research experience in areas such as complex network evolution and functional analysis, structural optimization strategies and propagation, and search routing control strategies. We have conducted detailed empirical analysis and modeling research on various space networks, including smart transportation networks, aviation networks, and power networks, achieving a series of innovative results.

Laboratory Director: Professor Han Dingding

Professor Han Dingding is a distinguished Professor at the School of Information Science and Engineering of Fudan University, a doctoral supervisor, and a secondary professor. He is a recognized expert in network and communication science, leading a key research group for the National Key Research and Development Program. He also serves as a dual-appointed professor at the Pujiang National Laboratory (in the field of artificial intelligence) and is a Vice President of the Chinese Society for Industrial and Applied Mathematics.

His extensive affiliations include being a Deputy Secretary-General of the Chinese Society for Interdisciplinary Sciences, a member of the Shanghai Society of Systems Simulation, a member of the Youth Innovation Expert Group of the Shanghai Science and Technology Association, and a member of the Committee of Extracurricular Mentors of Xi'an Jiaotong-Liverpool University. Professor Han is also an expert reviewer for "Electronics and Information" textbooks published by Peking University Press. Previously, he served as a member of the Degree Committee of the School of Information Science and Technology of East China Normal University, the Department Head of Communication Engineering (in charge of teaching), and the former academic leader of the Multi-Dimensional Information Processing Key Laboratory of Shanghai. He has conducted research and served as a visiting scholar at universities in the UK and the US.

Professor Han has received numerous honors, such as the National Excellent Textbook Award for Electronic Information, the Third Prize of the Shanghai Teaching Achievement Award, and the "Advanced Individual" award in the "Huawei Cup" China Graduate Mathematical Modeling Competition.

He has led research for several major national research projects, major natural science foundation projects, and key projects, accumulating rich real-world case studies and research experience. His expertise covers aspects such as complex network evolution and functional analysis, structural optimization strategies and propagation, and search routing control strategies. He has conducted detailed empirical analysis and modeling research on space networks like smart transportation networks, aviation networks, and power networks, achieving a series of innovative results. He has published over 100 papers in internationally authoritative journals, including IEEE/ACM Transactions, PLoS ONE, New Journal of Physics, Physical Review E, Scientific Reports, Chaos, Europhysics Letters, and Physica A. He is also the author of the monograph "Empirical Research on the Topology and Dynamics Behavior of Complex Networks" (Peking University Press, 2012), which explores complex networks across four dimensions: empirical research, modeling research, dynamics behavior research, and application research.

The team led by Professor Han maintains highly active international exchanges and collaborations. In connection with "Frontier Theories and Methods of Intelligent Information Processing" and "Complex Network Theory and Applications," they have established close academic exchanges and cooperation with research teams from prominent institutions worldwide. These include Professor H. E. Stanley from the U.S. Academy of Sciences, Northwestern University (USA), The Chinese University of Hong Kong, University of California Santa Barbara (USA), Polytechnic University of Catalonia (Spain), University of Nantes (France), Freiburg University (Germany), and City University of Hong Kong. This collaborative effort has resulted in the publication of over 30 high-level papers.

Requirements for Applicants

We are seeking candidates who meet the following criteria:

1. Have obtained or are about to obtain a doctoral degree (including those graduated from prestigious universities in mainland China or top 100 foreign universities). Generally, applicants should be no more than 35 years old. Preferred majors include data-driven modeling, basic physics, applied mathematics and statistics, automatic control, signal processing, and machine learning.
2. Are in good health, hardworking, diligent, and proactive. They should possess excellent academic and research achievements, outstanding independent analysis and problem-solving abilities, and an adventurous spirit.
3. Are proficient in English listening, speaking, reading, and writing skills, and possess strong programming ability.

Job Responsibilities

Successful candidates will be expected to:

1. Independently carry out academic research under the guidance of the laboratory leader.
2. Assist the laboratory leader in applying for projects, managing the laboratory, and assisting in guiding master's and doctoral students.