PhD Candidate in AI-Supported Long-Term Hydropower Scheduling

About this job

The Department of Electric Energy (IEL) at NTNU is seeking a highly motivated candidate for a full-time (100%) PhD position for 3 years as part of the Norwegian Centre on AI for Decisions. You will join the research group Electricity Markets and Energy System Planning (EMESP) at IEL, where we foster an open, inclusive, and collaborative working environment. Our work environment is defined by its friendly and supportive atmosphere, with regular gatherings such as professional meetings within the research group, weekly colloquia, shared lunches, and “Friday coffee” sessions to end the week. These formal and informal events offer opportunities to share ideas, celebrate milestones, and build relationships. PhD candidates also organize social activities open to everyone interested, fostering a welcoming and inclusive community. Your immediate Line Manager will be the Head of Department. About the project The position will be part of AID, the Norwegian Centre on AI for Decisions, an interdisciplinary national AI centre led by NTNU and SINTEF. AID brings together academic institutions, research organizations, and more than 50 professional organizations. Its primary objective is to advance AI for decision-making through fundamental research and real-world use cases, ensuring that AI-enhanced human decisions and autonomous systems are effective, safe, and trustworthy in sectors critical to society. This PhD project will contribute to AID by developing trustworthy AI-supported methods for long-term hydropower scheduling. Hydropower plays a central role in the Nordic power system, and deciding when to use or store water is becoming increasingly important as the energy system faces more uncertainty from weather, renewable generation, market developments, and future electricity demand. These decisions depend on uncertain inflows, future electricity prices, reservoir levels, cascade constraints, and the nonlinear relationship between water release and electricity production. Improving such decisions is essential for efficient renewable energy use, energy security, and the reliable operation of hydro-dominated power systems. The project will focus on how AI can support advanced optimization models for hydropower and energy-system planning. In particular, the research will investigate how the future value of stored water can be represented more accurately when hydropower production is nonlinear and when short-term operational conditions influence long-term reservoir decisions. In line with AID’s research areas, the project will emphasize trust, knowledge embedding, generalization under uncertainty, and human-interpretable decision support. The PhD candidate will develop and validate a hybrid methodology that combines established stochastic optimization with AI-based learning. The aim is not only to develop new algorithms, but also to understand how AI can be used safely and reliably in decision-support tools for the energy sector. The enhanced framework will be tested on Nordic hydropower use cases, including long-term and medium-term hydropower scheduling, multi-reservoir cascade operation, hydropower participation in energy and reserve markets, and planning under high renewable variability. In this way, the project responds to the growing need for safe and trustworthy AI tools in demanding, decision-critical systems. The PhD candidate will be hosted in the Electricity Markets and Energy Systems Planning (EMESP) group at the Department of Electric Energy, NTNU. The main supervisor will be Professor Hossein Farahmand, with co-supervision from experts in stochastic hydropower optimization and AI for decision-making, including Professor Arild Helseth, Associate Professor Jayaprakash Rajasekharan, Professor Sebastien Gros, and Research Manager Signe Riemer-Sørensen, depending on the final methodological focus of the PhD project. Duties of the position Carry out research of high quality within the framework described above Participate in activities of the EMESP research group Complete academic training consisting of coursework c

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