PhD scholarship in AI Agents in Electricity Markets: Equilibria, Dynamics, at Danmarks Tekniske Universitet

Kongens Lyngby, Capital Region of Denmark, Denmark -

Full Time

Start Date

Immediate

Expiry Date

05 May, 26

Salary

0.0

Posted On

04 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Optimal Control, AI Algorithms, Game Theory, Multi-Agent Systems, Learning, Optimization, Reinforcement Learning, Applied Mathematics, Power Systems, Electricity Markets, Programming Skills, Python, Julia

Industry

Research Services

Description

Are you interested in working at the intersection of optimal control, AI algorithms, and game theory, on problems that matter for real-world critical infrastructure? This PhD position offers the opportunity to develop strong competencies in multi-agent systems, learning and optimization, while addressing a timely and high-impact challenge: the growing use of AI agents in electricity markets. By joining our group of Dynamical Systems at DTU – department of Applied Mathematics and Computer Science, you will build rigorous mathematical and algorithmic tools to analyze how strategic, learning-based agents interact, and to design market rules that steer their learning dynamics toward stable, efficient, and safe outcomes. Project, Responsibilities and Qualifications The purpose of this PhD position is to understand how AI-driven, profit-seeking agents interact in electricity markets and how these interactions shape market equilibria and dynamics. The project focuses on analyzing learning-based strategic behavior in multi-agent settings and on designing market rules that ensure stable, efficient, and safe operation of critical infrastructure. The work combines theory and computation, with a strong emphasis on multi-agent systems, optimal control, and algorithmic market design. Your main tasks will include: Model AI-driven learning dynamics in electricity markets Analyze equilibria in terms of safety and learnability Develop algorithms to detect and characterize harmful market outcomes Design and test market rules that steer multi-agent learning dynamics Combine theoretical analysis with numerical experiments Communicate results through publications and presentations You should have a background in one or more of the following areas: Systems and Control Optimization / Operations Research Game Theory and Learning in Games Multi-agent Systems, Reinforcement Learning, or AI Algorithms / Applied Mathematics Power Systems or Electricity Markets (an advantage, not required) In addition, the candidate should have solid programming skills (e.g. Python, Julia) and an excellent command of English. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education. Assessment The assessment of the applicants will be made by Associate Professor Georgios Tsaousoglou. We offer DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. Salary and appointment terms The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years. The preferred starting date is 1 May 2026 or according to mutual agreement. The position is a full-time position. You can read more about career paths at DTU here. Further information Further information may be obtained from Associate Professor Georgios Tsaousoglou. You can read more about DTU Compute at www.compute.dtu.dk/ and the section for Dynamical Systems at www.compute.dtu.dk/sections/dynsys. If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. Application procedure Your complete online application must be submitted no later than 6 March 2026 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include: A letter motivating the application (cover letter) Curriculum vitae Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale You may apply prior to obtaining your master's degree but cannot begin before having received it. Applications received after the deadline will not be considered. All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position. DTU Compute DTU Compute – Department of Mathematics and Computer Science – is an internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting areas such as health, green transition, energy supply, and life science. We collaborate with universities, public and private organisations, and companies in Denmark and abroad, and through DTU’s startup ecosystem, we encourage innovation and entrepreneurship. We have a strong ethical, human, and sustainable approach that ensures integrity in our work. Therefore, we strive for and take responsibility for driving the democratisation of digital technologies, so that everyone has the opportunity to actively participate in the development, and we ensure a continued open, democratic, and inclusive society for the benefit of all. At DTU Compute, we value diversity, inclusion, and a flexible work-life balance. Read more about us at www.compute.dtu.dk. Technology for people DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.

Responsibilities

The main tasks include modeling AI-driven learning dynamics in electricity markets, analyzing equilibria, developing algorithms to detect harmful market outcomes, and designing market rules. The work combines theoretical analysis with numerical experiments and requires effective communication of results.