Skip to content

Careers at IDS

Post-doctoral Fellow in Complex Networks/Urban Science


Applications are invited for the appointment as a Post-doctoral Fellow in the HKU Musketeers Foundation Institute of Data Science (HKU-IDS) and the Department of Urban Planning and Design (Job Ref.: 515650), to commence as soon as possible starting Fall 2022 for at least one year, with the possibility of renewal. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.


  • Applicants should have a research background in complex networks, complex systems, urban data science, Bayesian inference, or statistical/theoretical/computational physics. Applicants with experience in complex networks are preferred, while individual research interests will also be taken into consideration.
  • Applicants should possess a Ph.D. degree in Data Science, Physics, Urban Planning, Computer Science, Math, Statistics, or related disciplines.
  • Applicants with extensive research experience and strong skills in mathematical and computational modelling are welcome to apply. Proficiency in coding languages such as Python, R, or C++ is preferred.


If interested, applicants may email their application package including an up-to-date C.V., a cover letter, and at least one representative work to Dr. Alec Kirkley ( Two references will be requested later on. Any enquiry can be sent to the same email address.

Apply Now

About the mentor

The appointee will be working under the supervision of Dr. Alec Kirkley, an Assistant Professor jointly appointed in the Institute of Data Science and Department of Urban Planning and Design at HKU. He obtained his PhD in physics at the University of Michigan, working with Mark Newman on complex networks and statistical physics, and did his undergraduate studies at the University of Rochester, beginning his studies in complex systems with Gourab Ghoshal. His research focuses on the theory of complex networks and the statistical physics of urban systems, with specific interests involving the characterization of structure in networks with metadata, the development of analysis methods and algorithms for statistical inference with network data, the structure and dynamics of human mobility, and the spatial manifestation of socioeconomic inequality. His research involves a balance of mathematical theory, computer simulation, and analysis of empirical data. His overarching goal is to develop physics-inspired mathematical and computational methods to aid in the understanding and modeling of complex networks and urban systems. More info can be found at his personal website,