PhD Candidate in AI-Supported Long-Term Hydropower Scheduling
Develop trustworthy AI methods for optimizing long-term hydropower scheduling in Nordic energy systems.
Best for:Best for master’s graduates in electrical power engineering, computer science, or operations research with strong quantitative skills and interest in AI-driven energy systems optimization.
Overview
This PhD position at NTNU’s Department of Electric Energy focuses on advancing AI-driven optimization for hydropower scheduling. The candidate will join the Electricity Markets and Energy System Planning (EMESP) group, contributing to the Norwegian Centre on AI for Decisions (AID). The project aims to enhance decision-making in hydropower systems by integrating AI with stochastic optimization, addressing challenges like nonlinear water-electricity relationships and uncertainty in renewable energy markets. The successful applicant will develop hybrid AI-optimization frameworks, validate them on Nordic hydropower use cases, and ensure trustworthy, interpretable decision support tools. The role offers a collaborative environment with mentorship, career development, and access to NTNU’s research infrastructure.
What you'll do
- Conduct high-quality research on AI-supported methods for long-term hydropower scheduling, integrating stochastic optimization and machine learning.
- Develop and validate hybrid AI-optimization frameworks for energy system planning, focusing on trust, interpretability, and generalization under uncertainty.
- Participate in the activities of the EMESP research group, including weekly colloquia, professional meetings, and collaborative projects.
- Complete academic training with a minimum of 30 ECTS in relevant courses, as part of NTNU’s doctoral programme.
- Contribute to publications in peer-reviewed journals and engage in popular science dissemination to communicate research findings.
- Present research at international conferences and conduct research stays at foreign institutions to foster academic collaboration.
- Potentially contribute to teaching, laboratory work, supervision, or examination duties to enhance career development opportunities.
Requirements
Degree required: Master’s degree in electrical power engineering, computer science, control engineering, chemistry with strong quantitative skills, managerial economics, or physics/mathematics with a specialization in operations research. Must correspond to a five-year Norwegian course with 120 ECTS at master’s level.
Degree offered: PhD
Language: English; B2 level (written and spoken) as per CEFR. Native speakers or those with equivalent proficiency are encouraged to document their skills.
Instruction: English
- Proven academic background with a minimum average grade equivalent to B or better on NTNU’s grading scale.
- Experience with optimization modeling and numerical optimization tools such as JuMP, Pyomo, Gurobi, CPLEX, or HiGHS.
- Strong programming and modeling skills, preferably in Julia or Python.
- Demonstrated ability to work independently, take initiative, and maintain discipline in research.
- Proficiency in English at B2 level (written and spoken) as per the Common European Framework of Reference for Languages (CEFR).
- Must have completed or be on track to complete a relevant master’s degree by autumn 2026.
- No specific nationality restrictions, but must meet NTNU’s requirements for admission to the Faculty’s Doctoral Programme.
- No age restrictions, but must demonstrate strong motivation and academic potential for doctoral studies.
Funding & terms
How to apply
Deadline: 2026-10-19
Applicants must submit a complete application package through NTNU’s electronic recruitment system. The application must include a cover letter, CV, academic transcripts, a list of publications (if any), and a statement of research interests. Additionally, applicants must provide documentation of their English proficiency if not a native speaker. Three references must be included, with contact information provided. The application must adhere to NTNU’s guidelines for recruitment positions.
- Cover letter
- CV
- Academic transcripts (BSc and MSc)
- List of publications (if applicable)
- Statement of research interests
- Documentation of English proficiency (if not a native speaker)
- Three reference letters with contact information
Contact
NTNU Recruitment· PhD Vacancy Contact
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