PhD Candidate in AI-Supported Long-Term Hydropower Scheduling

This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here.     Video: https://www.youtube.com/watch?v=_KHQjc4ndas&t=41s About the position Are you motivated to take a step towards a doctorate and open up exciting career opportunities? Do you have a background in electrical power engineering, operations research, or a related field, and are you interested in energy systems and markets? As a PhD candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, both in and outside academia.  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 corresponding to a minimum 30 ECTS • Contribute to publications in relevant journals and to popular science dissemination • Participate in international activities such as conferences and/or research stays at foreign educational institutions  Career-enhancing work, which is in addition to the research project and doctoral education, may be offered to a candidate who demonstrates clear motivation and ability for such work, and if the Department determines there is a need. Examples of career-enhancing work include, but are not limited to, contributing to teaching, laboratory and exercise teaching, supervision, and examination work within the employee's areas of competence.  Be prepared for changes to your work duties after employment. Required selection criteria • You must have a relevant Master's degree in either electrical power engineering, computer science, control engineering, chemistry, managerial economics with strong quantitative skills, or physics or mathematics with a specialization in operations research. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained at master's level. Master's students can apply, but the master's degree must be obtained and documented before starting the position and no later than autumn 2026. • You must have a strong academic background from your previous studies and have an average grade from your Master's degree study, or equivalent education, which is equal to B or better compared to  NTNU's grading scale . If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker grade background, you may be considered if you can document that you are particularly suitable for a PhD education, i.e., by having relevant work experience and/or published/publishable scientific papers. • You must meet the requirements for admission to the  Faculty's Doctoral Programme . • Experience with optimization modelling and numerical optimization tools, such as JuMP, Pyomo, Gurobi, CPLEX, HiGHS, or similar frameworks and solvers, will be considered an important qualification. • You must have English language skills, both written and spoken, corresponding to the scale  B2   in the Common European Framework of Reference for Languages (CEFR). Applicants who are not native English speakers are encouraged to document their English language proficiency. This can be done through an approved English language test. One of the following test scores could be documented for this purpose:  • TOEFL internet-based test (iBT) - Score equivalent to the B2 level: 79 - 101.  • IELTS - Score equivalent to the B2 level: 5.5 - 6.0  • Cambridge English - Score equivalent to the B2 level: 160 - 179. Further assessment of both written and oral English language skills, as well as the ability to communicate fluently, will be conducted throughout the selection process and during any interviews for all applica