HKUST-KTH Global Knowledge Network Awards
The Hong Kong University of Science and Technology (HKUST) and KTH Royal Institute of Technology (KTH) have identified each other as strategic partners to foster deep collaboration in research and education. The HKUST-KTH Global Knowledge Network Awards have been established to facilitate and support joint initiatives that will strengthen the two universities’ strategic priorities as well as develop multi-disciplinary cutting-edge research to create academic and societal impact. We invite proposals from researchers in all disciplines at HKUST and KTH, priority will be given to proposals in the areas of (i) Advanced Material Physics, (ii) Renewable Energy Technology, (iii) Robotics, (iv) Smart Cities, and (v) Sustainable Biotechnology & Green Chemistry.
Prospective applicants may sign up for the HKUST-KTH collaboration matchmaking to identify collaborators with similar research interests.
Call for Applications (2026)
Details
Applicants are encouraged to submit funding proposals for innovative and sustainable programs built around collaborative research linking HKUST and KTH. Proposals should be sustainable in the long-term with a plan for engagement that includes leveraging external funding and publication outputs.
In this round of awards, up to four (4) proposals will be funded, each with two Principal Investigators (one from HKUST and one from KTH). Each successful proposal may receive up to HKD 40,000 from HKUST and SEK 50,000 from KTH.
Eligibility
Applications are open to tenure-track Principal Investigators at HKUST and KTH-eligible Principal Investigators. For HKUST, full-time tenure-track faculty members from all disciplines are eligible to apply. Faculty in non-tenure-track positions (e.g., research or teaching tracks) are not eligible.
Previously awarded Principal Investigators who reapply in a subsequent round of the same Award will be given lower priority.
Emeritus and honorary faculty members, graduate students, post-docs, and research assistants/associates are not eligible to apply as Principal Investigators.
Application Submission
All applications must be submitted online via the HKUST-KTH Global Knowledge Network Awards 2026 - Application Form, and applicants should complete/ prepare the below documents and submit via the application form.
- Project Details*
- Project Budget*
* Both the “project details template” and “project budget template” can be downloaded from the cover page of the online application form.
Applicants should refer to the Awards Guidelines for details on eligible budget items, application selection criteria, and reporting requirements.
Important Dates
| Applications close | 16 October 2026, 23:59 HKT / 17:59 CEST |
| Successful applicants notified | By 16 November 2026 |
| Projects Start Date | 1 December 2026 |
| Projects End Date | 30 November 2027 |
| Final Report Due Date | 15 December 2027 |
For Enquiries
The Hong Kong University of Science and Technology
Judy Ma, VPRDO
Email: gkn@ust.hk
KTH Royal Institute of Technology
Hui Qu Jansons, International Relations Office
Email: hqj@kth.se
Awarded Projects
- Bridging Digital Finance and Urban Systems: Toward Joint Research on Tokenized Infrastructure and Sustainable Urban Development
HKUST PI: Pengyu ZHU, Division of Public Policy
KTH PI: Bertram Ingolf STEININGER, Division of Real Estate Economics and Finance - Radiomic MRI biomarkers for muscle quality assessment: advanced AI-powered approach for non-invasive clinical diagnosis
HKUST PI: Albert Chi Shing CHUNG, Department of Computer Science and Engineering
KTH PI: Ruoli WANG, Department of Engineering Mechanics - Spatiotemporal Profiling of Personal Particulate Matter Exposure in Global Cities: A Dual-City Case Study in Hong Kong and Stockholm
HKUST PI: Zhi NING, Division of Environment and Sustainability
KTH PI: Minghui TU, Unit of Systems and Component Design - Transparent Market Pricing in Hydro-Dominated Systems: Integrating Convex Hull Methods with Stochastic Scheduling
HKUST PI: Bo YANG, Department of Industrial Engineering and Decision Analytics
KTH PI: Mohammad Reza HESAMZADEH, Division of Electric Power and Energy Systems
- Generative Artificial Intelligence for Medical Imaging
HKUST PI: Hao CHEN, Department of Computer Science and Engineering, Department of Chemical and Biological Engineering and Division of Life Science
KTH PI: Rodrigo MORENO, Division of Biomedical Imaging - Understanding the Fracture Mode Competition in Additive Manufactured Martensitic Steel
HKUST PI: Tianlong ZHANG, Department of Mechanical and Aerospace Engineering
KTH PI: Levente VITOS, Unit of Properties - Accelerated Discovery of Single-Atom Catalysts for CO2 Reduction via Machine Learning
HKUST PI: Hanyu GAO, Department of Chemical and Biological Engineering
KTH PI: Mårten AHLQUIST, Department of Theoretical Chemistry and Biology - Unraveling the Relationship between Defects and Interfacial Thermal Transport in GaSb Epitaxial Layers
HKUST PI: Qiye ZHENG, Department of Mechanical and Aerospace Engineering
KTH PI: Yanting SUN, Department of Applied Physics - Low Energy, Always-on, Event-based Computer Vision with Neuromorphic Processing
HKUST PI: Bertram SHI, Department of Electronic and Computer Engineering
KTH PI: Jörg CONRADT, Division of Computational Science and Technology - Denoising Stochastic Solvers for Kinetic Equations through Filters
HKUST PI: Zhichao PENG, Department of Mathematics
KTH PI: Jennifer RYAN, Division Head for Numerical Analysis, Optimization and Systems Theory - Detecting Wildfire Emissions of Greenhouse Gases and Air Pollutants Using Multi-Sensor Earth Observation and Machine Learning: Addressing the Triple Challenges of Climate Change, Air Pollution and Public Health
HKUST PI: Xueying LIU, Division of Environment and Sustainability
KTH PI: Yifang BAN, Division of Geoinformatics