PhD Candidate in Optimal Utilization of Power Components and Grids using Digital Twin Norwegian University of Science and Technology in Norway

The Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU) invites applications for a PhD position focused on the optimal utilization of power components and grids using digital twin technology. This opportunity is part of the UPGriD project, which aims to develop advanced tools for real-time dynamic thermal rating, virtual sensing, anomaly detection, and predictive modeling of power-grid components. The project brings together leading research and industry partners, including SINTEF Energy Research, to address challenges in the electrification of society and the efficient use of existing power-grid infrastructure.

The successful candidate will join an interdisciplinary research environment that combines artificial intelligence, cybernetics, numerical modeling, and electric power engineering. The PhD research will span several technical work packages, contributing to methods applicable to transformers, power cables, and distribution substations. The position offers strong opportunities for scientific development, international collaboration, and close contact with industry partners.

Supervision will be provided by Professor Adil Rasheed, a leader in the field at NTNU. The research will focus on hybrid analysis and modeling, developing generic methods that integrate physics-based models, data-driven approaches, sparse measurements, and domain knowledge. Key research questions include optimal sensor placement, reconstructing high-resolution temperature fields from limited measurements, and building predictive models that account for uncertainty and unmodeled effects.

Applicants must hold a Master's degree in Mathematics, Cybernetics, Electrical Engineering, Computer Science, or a closely related field, equivalent to a five-year Norwegian course with 120 credits at the master's level. Master's students may apply if they can document completion before starting. A strong academic background is required, with an average grade of B or better on NTNU's grading scale or equivalent. Essential skills include Partial Differential Equations, Numerical Methods, Optimization, and Programming. Preferred qualifications include experience with finite element methods, numerical simulation, machine learning, data-driven modeling, scientific machine learning or physics-informed AI, and programming skills in Python or C++. Analytical and problem-solving abilities are highly valued.

The position is full-time and temporary, with a three-year employment period. The gross annual salary starts from NOK 550,800, with a 2% statutory contribution to the State Pension Fund. NTNU offers a mentor program for new employees, working capital for project implementation, and access to employee benefits and discounts. The university values diversity and encourages applications from candidates of all backgrounds, aiming to reflect the diversity of society and promote equality in scientific positions.

To apply, candidates must submit their application and all required attachments electronically via Jobbnorge.no by the deadline of 15th June 2026. Required documents include a CV, transcripts of academic records, a copy of the Master's thesis (or a working draft if recently submitted), documentation of completed Master's degree, publications, and contact information for at least two referees. If invited to interview, candidates must bring certified copies of certificates and diplomas. The application should clearly demonstrate how the candidate meets the criteria described in the position announcement.

NTNU is located in Trondheim, a modern European city known for its rich cultural scene, technological innovation, and high quality of life. The university is committed to creating knowledge for a better world and offers a supportive, inclusive, and international working environment. For further information about the position, contact Professor Adil Rasheed at [email protected].