Postdoctoral Fellow in Transport, Urban Environment and Spatial Data Science

University of Hong Kong Hong Kong, Hong Kong

About the position

Postdoctoral Fellow in Transport, Urban Environment and Spatial
Data Science (Ref. No. 537709)

PEACE Research Group | Department of Geography | The University
of Hong Kong (HKU)

Applications are invited for a Postdoctoral Fellow position
in the PEACE Research Group at the Department of Geography, The
University of Hong Kong (HKU). The appointment will commence on a
mutually agreed date.

The PEACE Research Group conducts interdisciplinary research at the
intersection of transport, environment, urban systems and human
populations, with a particular emphasis on the application of
spatial data science and emerging computational methods. Our team
brings together researchers from diverse backgrounds, including
engineering, data science, urban planning, public health and
economics. We foster an open, collaborative and stimulating
research environment that encourages interdisciplinary learning and
innovation.

HKU and its Geography discipline are both ranked
11thglobally in the QS rankings.

Qualifications and Requirements

Applicants should possess:

  • A Ph.D. degree (awarded or nearing completion) in

Transportation, Environmental Science, Urban Planning, Data
Science, Public Health, or a closely related discipline.

  • Strong quantitative analytical and problem-solving skills, with

relevant research experience in urban environments, spatiotemporal
data analysis, artificial intelligence or related fields.

  • Excellent academic writing and communication skills in English,

with a demonstrated record of publications in high-quality
international peer-reviewed journals.

  • Strong self-motivation, independence, teamwork and project

management skills, with the ability to initiate and conduct
research and contribute to interdisciplinary collaborations.

Preference will be given to candidates with experience in
large-scale or cross-regional research, advanced AI or
computational methods, or an outstanding publication record in
leading international journals.

Research Areas

Potential research directions include, but are not limited
to:

  • Transport, Environment and Urban Systems
  • AI-enabled Urban and Data-driven Research
  • Human Mobility, Environmental Exposure and Population

Health

Methods and Technical Expertise

Relevant methodological expertise may include:

  • Applied AI, Machine Learning and Emerging Computational

Methods

  • Environmental Monitoring and Smart Sensing (Indoor/Outdoor,

Mobile/Fixed)

  • Remote Sensing, GIS and Spatial Analytics
  • Spatiotemporal Big Data Analytics and Modelling

Candidates are not expected to have expertise in all the above
areas. We particularly welcome applicants who can bring
complementary disciplinary perspectives and methodological
strengths to the research group.

A highly competitive salary commensurate with qualifications and
experience will be offered, in addition to annual leave and medical
benefits.

The University only accepts online applications for the above post.
Applicants should apply online and upload (1) an up-to-date
curriculum vitae (CV); (2) representative research publications;
(3) a research statement or proposed research plan, outlining their
research interests, relevant expertise and potential research
directions; and (4) at least two academic reference letters from
referees familiar with their research experience and academic
potential.

Review of applications will start as soon as possible and continue
until November 30, 2026, or until the post is filled, whichever is
earlier.

The University thanks applicants for their interest but advises
that only candidates shortlisted for interviews will be notified of
the application results.

For enquiries about the position, please contact Dr. Junshi Xu at
junshixu@hku.hk .

Advertised: Oct 21, 2026 (HK Time)

Applications close: Nov 30, 2026 (HK Time)

This listing was collected from a public source and is reproduced here for information only. Always confirm the details on the original posting before applying.
View the original posting

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