PhD Candidate in Multimarket Bidding Decision Support in Nordic Power Markets
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Video: https://www.youtube.com/watch?v=_KHQjc4ndas&t=41sAbout the positionAre 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 (aiD). You will join the Electricity Markets and Energy System Planning (EMESP) research group 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 projectThe position will be part of aiD, the Norwegian Centre on AI for Decisions, an interdisciplinary national AI center 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 multimarket bidding and decision support in Nordic power markets. The rapid integration of wind power, battery storage, and other flexible resources is creating new opportunities for market participation, but also more complex decision-making problems. Energy producers increasingly need to coordinate decisions across day-ahead, balancing, and other electricity markets while dealing with uncertain renewable generation, activation needs, regulation and imbalance prices, and rapidly changing market conditions. Recent developments such as 15-minute market time units, automated mFRR energy activation, and flow-based market coupling further increase the need for decision-support methods that are fast, robust, risk-aware, and suitable for real-time operation.
The project will focus on how AI and mathematical optimization can be combined to support sequential bidding decisions under uncertainty. The initial use case will consider a wind power operator with battery storage participating in the day-ahead market and the mFRR capacity and energy activation markets. The research will investigate how probabilistic forecasts, market-state and regime information, and the future value of battery flexibility can be incorporated into bidding decisions. A central scientific question will be to determine which parts of the decision problem are best handled by data-driven learning and which should remain within structured optimization. In line with AID’s research areas, the project will emphasize knowledge embedding, uncertainty representation, risk-aware decision-making, computational efficiency, generalization under changing market conditions, and safe constraint handling.
The PhD candidate will develop and validate decision-support methods based on deep reinforcement learning, stochastic optimization, and hybrid combinations of learning and optimization. Learning-based policies will be compared with equivalent rolling stochastic optimization benchmarks operating with the same information and operational constraints. The project will also explore how AI can be used to approximate, accelerate, contextualize, or enhance optimization, for example by learning future flexibility value, selecting relevant uncertainty scenarios, or reducing computational complexity. The methods will initially be tested for coordinated day-ahead and mFRR market participation and may later be extended to more strongly coupled market combinations and other flexible energy resources. The project therefore offers the opportunity to work at the intersection of artificial intelligence, optimization, electricity markets, and renewable energy systems, while addressing the growing need for trustworthy and computationally practical AI-supported decision making in future power markets.
Duties of the positionCarry out research of high quality within the framework described aboveParticipate in activities of the EMESP research groupComplete academic training consisting of coursework corresponding to a minimum 30 ECTSContribute to publications in relevant journals and to popular science disseminationParticipate 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 criteriaYou must have a relevant Master's degree in either electrical power engineering, control engineering, 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 scientific papers.You must meet the requirements for admission to the Faculty's Doctoral Programme.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 applicants.
The appointment is to be made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position.
Preferred selection criteriaWork and/or research experience in electricity and power markets, imbalance forecasting, reinforcement learning, and operations research or optimization.Excellent programming and modeling skills, preferably in Julia, Python, or a similar programming language.English language skills, both written and spoken, corresponding to the scale C1 in the Common European Framework of Reference for Languages (CEFR). See which scores are equivalent to the C1 level here.
Personal characteristicsTo complete a doctoral degree (PhD), the candidate is expected to:
demonstrate strong motivation, curiosity, and a learning-oriented mindsetwork independently, take initiative, and maintain good structure and discipline in their workcommunicate effectively and collaborate well with supervisors and peersshow resilience and work constructively when facing challenges or setbacksdemonstrate integrity and a strong sense of responsibility in their research conductEmphasis will be placed on personal and interpersonal qualities.
We offerAn exciting job with an important mission in societyDeveloping tasks in a strong and international professional environmentCareer guidance and follow-up during the PhD periodOpen and inclusive working environment with committed colleaguesSocial activities organized by PhD StudentsWeekly social gathering on Fridays (Friday coffee)Working capital that can be used to implement the projectMentor programme as a new employee at NTNUFavorable terms as a member of the Norwegian Public Service Pension Fund (SPK)Free Norwegian language training at a basic level (A2)As a PhD Candidate at NTNU, you will have access to employee benefits.
DiversityDiversity is a strength, and at NTNU we aim to be an e
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