Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 15 August 2026 or later. You can submit your application via the link under 'how to apply'.

Title:
PhD position on Digital Twins for AI-driven Water-based District Cooling Systems to boost Megacity Sustainability

Research area and project description:
With the acceleration of urbanization on a global scale, combined with the ever-worsening climate crisis, major challenges exist in the search for resilient and sustainable solutions in the areas of energy and water. In particular, urban populations in tropical regions are growing rapidly, causing sharp increases in cooling demand, exacerbating urban heat island effects, and placing immense pressure on local energy infrastructure. Water-borne (“hydronic”) approaches to cooling are more environmentally sustainable compared to fossil fuel- and refrigerent-based approaches, and hydronic cooling has seen adoption largely in buildings and complexes with individual chiller plants. While interest in hydronic district cooling systems increases, deployment must improve significantly when scaling from building to district and even city level.

We are inviting applications for a PhD position based in the Sustainable Water-based Cooling in Megacities (SWiM) project, funded by the Grundfos Foundation. SWiM is a collaboration between Aalborg University (AAU), Aarhus University (AU), Nanyang Technological University (NTU), and Grundfos itself as an industry partner.

This doctoral project will investigate exciting new ways of digitalizing district cooling systems in megacity environments with hot and humid climates through Digital Twins (DT), including system design and installation support, optimized control, system monitoring, and fault detection, with a major emphasis on scientifically sound modelling and prediction, together with rapid replicability so that solutions can be quickly deployed in new districts.

This raises fascinating research challenges that will be addressed as the core activities of the PhD position:

• Investigate information modelling requirements for effective Digital Twinning of district cooling systems that spans from building to district scales i.e. Building Information Modelling (BIM), Geographic Information System (GIS) modelling standards
• Develop effective algorithms that extract and align the right semantic, geometric, and topological information from diverse models and data sources to be used in cooling system Digital Twins
• Develop evidence-based methods for system design and installation support, e.g. injecting configuration faults into BIM models to predict problems with real-world installations
• Towards rapid replicability, exploit and extend the Digital Twins as a Service (DTaaS) platform for creating and deploying cooling system digital twins by creating reusable DT assets tailored to BIM and GIS
• Develop services for online monitoring and prediction of expected demand and anomaly properties, utilising the FMI-supported Maestro framework for co-simulations between adequate digital twin models (DTaaS), and (initially) the statistical model checking engine of UPPAA
• Through DTaaS, develop new methods that orchestrate AI-driven analysis tools and simulators for multi-scale cooling system prediction, optimized control, monitoring and fault detection, in collaboration with AAU and Grundfos

Work will be undertaken in close collaboration with NTU ERI@N who will develop realistic multi-scaled demonstrators (room, floor, building, district levels) for real world-scale testing, verification, and validation.

• Project description. For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application.

Qualifications and specific competences:
Required Qualifications:

• A Master’s degree in Computer Science, Computer Engineering, or a related field.
• Strong mathematical foundations and software programming skills (e.g., Python, Java, C++).

Preferred Qualifications:

• Interest or experience in geospatial analysis, Building Information Modelling, cyber-physical systems or digital twin platforms.
• Familiarity with simulation tools, AI/machine learning libraries, or modelling frameworks.
• Experience with formal model checking tools such as UPPAAL, SPIN, PRISM, TLA+ (TLC), Alloy Analyzer, or similar systems.
• Familiarity with SMT solvers (e.g., Z3) is also relevant in the context of symbolic and bounded model-checking workflows.

Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is Department of Electrical and Computer Engineering, Helsingforsgade 10, 8200 Aarhus N, Denmark. 

Contacts:
Applicants seeking further information regarding the PhD position are invited to contact:

• Professor Peter Gorm Larsen, Pgl@ece.au.dk (main supervisor)
• Associate Professor Carl Schultz, cschultz@ece.au.dk (co-supervisor)

For information about application requirements and mandatory attachments, please see our application guide. If answers cannot be found there, please contact:

• admission.gradschool.tech@au.dk

How to apply:
Please follow this link to submit your application.

Application deadline is 14 May 2026 at 23:59 CEST.

Preferred starting date is 15 August 2026.

Please note:

• Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be taken into account.
• The programme committee may request further information or invite the applicant to attend an interview.
• Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.