PhD Stipend in Intelligent Industrial Energy Systems, Physics-informed AI, digital twins and advanced control

Help build the systems that will electrify industry Industrial energy systems are about to change fundamentally. For more than a century, industrial processes have been designed as isolated, deterministic systems. You model them, you optimize them, you operate them. That paradigm is breaking down. As electricity replaces fossil fuels, industrial systems must operate under fundamentally new conditions: fluctuating renewable energy, dynamic electricity prices, and increasing system complexity. In the future, industrial energy systems will not simply run. They will understand themselves. They will learn from data. They will anticipate and adapt. They will optimize continuously. At Aalborg University, we are starting to build the scientific foundations for those systems. We are looking for a PhD researcher who wants to be part of that. The stipend is open for appointment from 15 September 2026 or soon hereafter. The duration of the position is three years. What this PhD is really about This PhD focuses on modelling and controlling electrified industrial thermal systems under real-world conditions. The work is embedded in a collaborative research project with industrial partners, providing access to realistic system configurations, experimental setups and operational data. The scientific ambition is to develop methods that combine physical models and data-driven approaches for adaptive, real-time operation of complex energy systems. Applications such as drying processes serve as test cases, but the goal is to establish methodologies that extend across industrial thermal systems more broadly. How do we create industrial energy systems that combine physics and data to become adaptive, autonomous and trustworthy? To get there, you will work at the intersection of thermal energy systems, machine learning and scientific AI, system modelling and identification, as well as optimization and control. Your work will focus on developing new methods that integrate first-principles models with data-driven learning, supported by reduced-order representations and designed for real-time optimization and control. In essence, this is about physics-informed AI-approaches where physical insight and data reinforce each other rather than compete. The ambition is to move beyond abstract or idealized solutions. Think digital twins that are not just conceptual, but genuinely useful in practice. Think control systems that can operate robustly under uncertainty, rather than relying on simplified assumptions. The systems you will be working with are real world, industrial scale energy systems rather than simplified lab set-ups. They include high-temperature, high-power applications, including heat pumps, thermal storage, and complex process units, where both physical understanding and intelligent control are essential. What you will do Your day-to-day research will move between theory, modelling and real systems. You will build mathematical and physics-based models of complex thermal systems. You will develop reduced order representations enabling real-time implementation suited for online optimization of operating conditions and control. You will combine the models with data driven learning to adapt to varying operating conditions. You will design control strategies that can operate under real-world constraints. Some of your work will be purely theoretical. Some of it will be validated against experimental setups and industrial data. You will publish your work, present it internationally, and build a research profile that is relevant far beyond this specific application. The environment you will step into You will not be placed inside a narrow, predefined research group. Instead, you will work across a small, complementary team that brings together different pieces of the puzzle: thermal systems and physical modelling advanced control and system-level thinking industrial electrification and future energy technologies This is an emerging collaboration, not a finished structure. This means that you are not just executing a predefined roadmap. You are helping shape the direction. Your supervisors You will be supported by a team with different strengths, scientifically and personally. Henrik Sørensen is Head of Section and your main supervisor. His focus is not only on research, but on building researchers. He will push you, give you space, and expect you to grow into an independent scientist. Henrik C. Pedersen is Vice Head of Research and one of the key profiles within modelling and control at AAU. He will challenge your thinking, sharpen your methods and connect your work to a broader scientific context. Anna Lyhne Jensen represents the next generation of energy researchers. Her work focuses on electrification of industrial processes and future thermal systems, and she is deeply engaged in the green transition. She will be your closest collaborator in many parts of the project. Together, the team is not uniform, and that is exactly the point. No single discipline can solve this problem. Who you are You likely have a background in energy engineering, mechanical engineering, control, applied mathematics, physics or a related field. But more importantly, we are looking for someone who: Has a strong curiosity about how complex systems actually work. Is comfortable with mathematical modelling and analytical thinking. Is motivated to combine physical insight with data-drive methods. Enjoys building models and methods – not just analyzing existing ones. Has the ambition and persistence to tackle difficult problems and carry them through. This is a demanding PhD. It requires both independence and engagement, and a willingness to work at the intersection of disciplines. You don’t need to know everything from the start - but you should have a clear drive to develop expertise and contribute at a high scientific level. What you get out of it This PhD is not just about writing three papers and moving on. It is about building a profile that sits at the intersection of energy systems, AI & modelling and optimization & control. That combination is rare, and extremely valuable, both in academia and industry. You will work with real systems, real data and real problems. You will collaborate with industry. You will be part of the broader European research landscape in energy and sustainability. And you will have a high degree of freedom to shape your own direction. Why this matters Industrial heat is one of the hardest parts of the green transition. Electrification alone is not enough. If we cannot control and optimize these systems intelligently, we will not unlock their full potential. This PhD is about contributing to these missing pieces. Expected starting date is 15 September 2026 or as soon as possible thereafter. Interested? If you want to work on something that is technically challenging, scientifically interesting, and actually matters — we would like to hear from you. Applications should include a CV, transcripts and a short motivation. For further information, feel free to contact: Henrik Sørensen, hs@energy.aau.dk Qualification requirements PhD stipends are allocated to individuals who hold a Master's degree. PhD stipends are normally for a period of 3 years. It is a prerequisite for allocation of the stipend that the candidate will be enrolled as a PhD student at: the Doctoral School of Engineering and Science in accordance with the regulations of Ministerial Order No. 1124 of September 19, 2025 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress of the PhD student shall be assessed at regular points in time. As part of the PhD study, you are among other things required to complete PhD courses corresponding to 30 ECTS, gain experience with teaching or other forms of knowledge dissemination and complete an external research stay outside of Aalborg University, preferably 3-6 months at a foreign