Promovendus in AI en kwantuminspiratie-optimalisatie voor duurzame energiesystemen

Job Description

Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers of tomorrow and creating novel solutions to major global challenges. Our community is made up of 16 000 students and 5 200 employees, including 446 professors. Our campus is in Espoo, Greater Helsinki, Finland.

Diversity is part of who we are, and we actively work to ensure our community's diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community.

We are now looking for a fully funded Doctoral Researcher to work on AI-based and quantum-inspired methods for sustainable energy systems. The position is funded by the Research Council of Finland (RCF) and is part of the project "Smart, Economic, and Sustainable Energy Management of Green Maritime Industry via AI Approaches" (SiGMA). The position is hosted by the Multi-energy System Planning and Operation Research Group at Aalto University School of Electrical Engineering.

The research will focus on the modelling, optimization, and operation of AI+Energy systems, integrated energy systems, green hydrogen, ship and port energy systems, and energy-intensive infrastructures such as data centres. The research group is led by Assistant Professor Zhengmao Li and focuses on multi-energy system optimization, AI-driven energy management, green hydrogen integration, and sustainable maritime energy systems. The position will also benefit from the group's ongoing research activities and collaborations, and will provide opportunities to work with academic and industrial partners in Finland and internationally.

Your role and goals

In this position, your doctoral research will focus on developing AI-based and quantum-inspired methods for the modelling, optimization, and operation of sustainable energy systems. The main application area will be green maritime energy systems, especially ship and port energy systems with integrated electricity, heat, hydrogen, storage, and flexible loads.

Your main tasks will include:

• Developing mathematical, data-driven, and AI-based models for sustainable multi-energy systems.
• Designing optimization and decision-making methods for system planning, scheduling, and operation.
• Exploring quantum-inspired optimization methods for selected energy-system problems.
• Applying the developed methods to case studies related to ship and port energy systems, green hydrogen, smart grids, or other energy-intensive infrastructures.
• Implementing algorithms and simulation models using Python, MATLAB, Julia, or other suitable tools.
• Analysing simulation and project data, and validating the developed methods through practical case studies.
• Preparing scientific publications for international journals and conferences.

Your network and team

In this position, you will join the Multi-energy System Planning and Operation Research Group at Aalto University School of Electrical Engineering. Your main supervisor will be Assistant Professor Zhengmao Li.

You will work closely with:

• Your supervisor and research group, including doctoral researchers, master's students and research assistants working on energy-system modelling, optimization and AI-based methods.
• Academic collaborators at Aalto University and partner universities in Finland and abroad.
• Industrial and societal stakeholders, especially in areas related to energy systems, hydrogen, maritime operations, smart infrastructure and data-driven decision-making.
• The wider Aalto community, including researchers and students from electrical engineering, automation, computer science and energy-related fields.

The working style of the group is collaborative and research-oriented. You will be encouraged to develop independent scientific ideas, discuss your work regularly with the team, contribute to joint publications and participate in project meetings, seminars and stakeholder activities.

Your experience and ambitions

Candidates should hold a Master's degree in electrical engineering, energy systems, automation, or a related field. A strong background in energy-system modelling, mathematical optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated.

Experience with programming and computational modelling is important for the position. Familiarity with Python, MATLAB, Julia, or similar tools is considered valuable. Previous experience in one or more of the following areas is an advantage: power and energy systems, integrated multi-energy systems, hydrogen systems, maritime or port energy systems, data centre energy management, smart grids, stochastic optimization, deep learning, safe reinforcement learning, or quantum-inspired optimization.

A strong command of English, good communication skills, and the ability to work both independently and as part of a research team are essential. The candidate is expected to be motivated to publish high-quality scientific papers, participate in research projects, collaborate with academic and industrial partners, and develop into an independent researcher during the doctoral studies.

The following skills are considered assets, but they can also be developed during the doctoral studies:

• Experience with energy-system simulation or optimization tools.
• Experience with machine learning, reinforcement learning, or other AI-based methods.
• Knowledge of power systems, hydrogen systems, or multi-energy system operation.
• Familiarity with uncertainty modelling, stochastic programming, robust optimization, or real-time decision-making.
• Interest in quantum computing or quantum-inspired optimization methods.
• Good programming skills in Python, MATLAB, Julia, or similar languages.
• Experience in writing scientific papers or participating in research projects.

What we offer

We offer a fully funded doctoral researcher position in a timely and growing research area at the intersection of artificial intelligence, quantum-inspired methods, and sustainable energy systems. You will have the opportunity to work on research questions that are both scientifically challenging and closely connected to the clean energy transition, including integrated energy systems, green hydrogen, ship and port energy systems, smart grids, and energy-intensive infrastructures such as data centres.

Aalto University offers an international, multidisciplinary, and supportive research environment. Our work is guided by responsibility, courage, and collaboration, and we value openness, equality, inclusion, and well-being. You will receive supervision, feedback, and support from your supervisor and research group, while also being encouraged to develop independence as a researcher.

The position also offers good opportunities for professional development, including doctoral courses, research training, teaching experience, conference participation, international collaboration, and skill development in scientific writing, project work, and academic communication. The primary workplace is the Otaniemi campus in Espoo, Finland, which offers a vibrant academic environment, modern research facilities, good public transport connections, and access to the wider innovation ecosystem around Aalto University.

Join us!

To apply, please share your CV and motivation letter with us through our recruitment site ("Apply now!") latest at 23.59 pm (EET) 30th August 2026. We will go through applications, and we may invite suitable candidates to interview already during the application period. The position will be filled as soon as a suitable candidate has been found.

Please note: Aalto University's employees should apply for the position via our internal HR system Workday (Internal Jobs) by using their existing Workday user account (not via the external webpage for open positions). If you are a student or visitor at Aalto University, please apply with your personal email address (not aalto.fi) via Aalto University open positions.

For more information about the role, please contact Zhengmao Li +358 50 441 3955, or [email protected]. For questions related to the application process, please contact HR advisor [email protected].

We aim to have a transparent and equal recruitment process, so feel free to ask us for feedback.