Summary
Aalborg University (AAU) is offering a 3-year PhD stipend in Digital Twin Modelling and Multi-Timescale Optimization of Electrolysis Systems. This unique opportunity, affiliated with the Pioneer Center for Accelerating P2X Materials Discovery (CAPeX), focuses on building the digital backbone for future Power-to-X plants. The successful candidate will develop system-level digital-twin and optimization methodologies to enhance the efficiency, flexibility, and economic viability of Power-to-X operations, collaborating with leading international universities.
PhD Stipend in Digital Twin Modelling and Multi-Timescale Optimization of Electrolysis Systems at Aalborg University
Designation
PhD Stipend
Research Area
- Digital Twin Modelling
- Multi-Timescale Optimization
- Electrolysis Systems
- Power-to-X Systems
- Renewable Energy Integration
- Energy System Optimization
Location
Aalborg, Denmark
Eligibility/Qualification
Required Competencies:
- A relevant Master’s degree in energy engineering, electrical engineering, mechanical engineering, chemical engineering, control engineering, or a related discipline.
- Strong foundation in at least one of: electrochemical or energy-system modelling, dynamic modelling and control, mathematical optimization, or data-driven modelling.
- Experience with numerical modelling, simulation, optimization, control, or engineering-data analysis.
- Good programming skills in Python, MATLAB/Simulink, or a comparable scientific computing environment.
- Fundamental understanding of energy-conversion systems, thermodynamics, electrical engineering, heat and mass transfer, electrochemistry, or control engineering.
- Ability to conduct independent, systematic, and scientifically rigorous research while working effectively in a multidisciplinary team.
- Good written and spoken English.
Advantageous Experience (not all required):
- Electrolyzer technologies
- Digital twins
- Model order reduction
- System identification
- Power electronics
- Model predictive control
- Multi-objective optimization
- Machine learning
- Renewable-energy integration
- Experimental testing
- Hardware-in-the-loop implementation
Qualification Requirements:
- Hold a Master’s degree.
- Enroll as a PhD student at the Doctoral School of Engineering and Science according to Ministerial Order No. 1124 of September 19, 2025.
- Complete PhD courses corresponding to 30 ECTS.
- Gain experience with teaching or other forms of knowledge dissemination.
- Complete an external research stay outside of Aalborg University (preferably 3-6 months at a foreign research institution).
Scholarship Description
This 3-year PhD position is based at the Department of Energy, Section for Power Electronics System Integration and Materials at Aalborg University. The role involves developing and validating digital-twin and optimization methods for electrolysis systems, focusing on physics-based, data-driven, or hybrid digital twins to capture electrochemical, thermal, flow, and system-level behavior. The project aims to build computationally efficient models for monitoring, performance prediction, optimization, and control, and to investigate interactions across various operational timescales. The successful candidate will develop optimization strategies to improve electrolyzer performance, efficiency, flexibility, and economic operation under variable electricity supply and renewable-energy integration. Collaboration with academic and industrial partners and dissemination of results through publications and conferences are key aspects. A limited amount of teaching and student supervision may also be included.
How to Apply
Applications must be submitted via Aalborg University’s recruitment system. The application must include:
- Application, stating reasons for applying and qualifications.
- Curriculum Vitae (CV).
- Diplomas (bachelor’s and master’s degree diploma, including grades).
- A project description (3–5 pages) outlining initial thoughts and ideas on the project, including a brief state-of-the-art with references, a time schedule, and how the project objectives will be addressed.
- Publications, if applicable.
- Other relevant documents.
Last Date for Apply
October 16, 2026
Apply Link
