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Peter Gorm Larsen

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PhD Position in AI-driven Multiscale System Modelling for IoE Digital Twin at Aarhus University Aarhus University in Denmark

I am recruiting a fully funded PhD student in AI-driven multiscale system modelling for IoE digital twin at Aarhus University.

Keywords

Computer Science
Electrical Engineering
Information Technology
Digital Twin Technology
Internet Of Things
energy storage systems
Machine learning

Description

Aarhus University’s Department of Electrical and Computer Engineering is offering a fully funded PhD position in AI-driven Multiscale System Modelling for IoE Digital Twin, starting 1 May 2026 or later. This opportunity is part of the Marie Skłodowska-Curie Doctoral Network SAILING, focusing on the development of advanced digital twin technologies for the Internet-of-Energy (IoE). The project addresses the challenges of modelling complex, dynamic, and decentralized energy systems, supporting the global transition to clean energy and decarbonization. The research will involve developing first-principle modelling techniques to simulate nonlinear dynamics of IoE devices, designing graph neural network (GNN)-based frameworks to capture interdependencies among IoE components, and integrating these into a unified digital twin capable of multiscale system representation. Collaboration with other Early-Stage Researchers in the SAILING network is expected, with complementary topics such as low-latency data transmission, AI-based uncertainty analysis, and efficient model execution. Candidates should hold a Master’s degree in Computer Science, Computer Engineering, or a closely related field, with strong mathematical and programming skills. Experience in system modelling, numerical simulation, machine learning, graph-based models, and energy systems is highly desirable. EU Marie Curie eligibility rules apply, including mobility requirements and doctoral status restrictions. The position is fully funded, including salary, allowances for living, mobility, and family (if applicable), and pension contributions, as per EU Work Programme rates. The place of work is IT City Katrinebjerg, Aarhus, Denmark. Applications must be submitted online before 30 January 2026, with all required documents uploaded, including the project description. For further information, contact Professor Peter Gorm Larsen or Associate Professor Cláudio Gomes. This PhD position offers a unique opportunity to contribute to cutting-edge research in AI, digital twins, and energy systems, within a highly international and collaborative environment at Aarhus University.

Funding

The position is fully funded as part of the Marie Skłodowska-Curie Doctoral Network SAILING. Salary, holiday payment, and pension contributions are included, with allowances for living, mobility, and family (if applicable) as per EU Work Programme rates. Salary and terms of employment follow the applicable collective agreement and include a non-pensionable PhD supplement. Country correction coefficient applies.

How to apply

Submit your application via the provided link before the deadline. Upload all required documents, including the project description as a PDF. Only applications received before the deadline will be considered. For questions, contact the listed supervisors.

Requirements

Applicants must have a Master’s degree (120 ECTS) in Computer Science, Computer Engineering, or a closely related field. Strong mathematical foundations and programming skills (e.g., Python) are required. Experience in system modelling, numerical simulation, or complex systems is expected. Preferred qualifications include background in machine learning, graph-based models, uncertainty quantification, and interest or experience in energy systems, IoT, or digital twin platforms. Familiarity with simulation tools, AI libraries, or modelling frameworks is advantageous. EU Marie Curie eligibility rules apply: candidates must not already possess a doctoral degree and must not have resided or carried out their main activity in Denmark for more than 12 months in the 36 months prior to recruitment.

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