About this role
About the position We invite applications for position as PhD Research Fellow in computer science/AI for energy informatics available at the Department of Informatics (IFI), UiO Starting date: as soon as possible, no later than Sept. 2026. The fellowship period is three years. Depending on the candidate and the teaching needs of the department, the fel-lowship period can be extended either for compulsory work consisting of e.g., teaching and supervision duties and research assistance up top four years. No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Place of work is the Department of Informatics at Forskningsparken, Oslo. Job description Description about scope of the PhD research: Do you have a background in energy informatics, computer science, or energy systems, and are you are interested in edge intelligence, multi-agent AI, and interdisciplinary collaborative research, and looking for a PhD position, this opportunity can be for you. The Energy Informatics EI@ND Networks and Distributed Systems group at the Department of Informatics (IFI), University of Oslo (UiO) is seeking a highly motivated PhD candidate for a fully funded full time PhD position for the Norwegian National AI Center on AI for Decisions (aiD). The fellowship is for research training for a period of 3 years leading to the successful completion of a PhD degree. As a PhD candidate with us, you will gain valuable experience opening up exciting career opportunities in academia and industry. About the Project: The position will be part of AID, the Norwegian Centre on AI for Decisions , an interdisciplinary national AI centre in Norway 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. The global energy system is undergoing a profound transformation driven by the rapid uptake of distributed renewable energy resources and the envisioned empowerment of prosumers. Thus, the traditional centralized grid is evolving into a highly distributed, data/ computation-intensive, AI-driven ecosystem in which smart meters, microgrids, aggregators, and edge devices actively participate in energy production, storage, consumption, and market interactions. This transition is fundamentally reshaping the structure of energy networks, shifting from centralized control to distributed, prosumer-driven ecosystems where interconnected multi-agents interact strategically in dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently multi-agent, strategic, and resource-constrained. Each agent has its own objective (e.g, cost, profit, comfort, sustainability, etc.) and interacts with other agents for shared resources (e.g., grid capacity, energy prices). In this new paradigm therefore, multiple autonomous agents including households, aggregators, and grid operators-must make real-time, interdependent decisions under shared constraints and competing objectives. Multi-agent learning and optimization show promise in this regard. Yet, the deployment of AI for decision making in critical infrastructure like the energy sector introduces challenges related to safety, fairness, accountability, transparency, and explainability (FATE), as well as compliance with emerging regulatory frameworks such as the EU AI Act and Data Act. More About the Position: In this project, you will develop game-theoretic AI frameworks by integrating Data-driven intelligence with principled decision structure for multi-agent decision making in energy systems. In addition, you will derive formal FATE metrics for the energy system and develop Explainable AI solutions to ensure transparency while also addressing privacy preservation and computation-efficiency requirements at the energy edge. Further, you will incorporate compliance-by-design AI architectures and models and validate our solutions across key energy use cases such as energy market optimization (demand response, transactive energy peer-to-peer trading, and renewable integration) and (energy edge+) distribution grid resilience. Duties of the position: Carry out research of high quality within the scope and framework described above Actively Participate in activities of the Energy Informatics/ND research group Complete academic training consisting of coursework corresponding to a minimum 30 ECTS Produce publications of exceptional quality in relevant conferences, journals and actively contribute in popular science dissemination Participate in international activities such as conferences and/or research stays in foreign educational institutions What skills are important in this role? The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials. Required qualifications: Master’s degree (120 credits equivalent of the Norwegian Master’s degree program) in Electrical Engineering, Communication Engineering, Computer Science or other relevant discipline, with a Master Thesis in the Master’s degree program. Foreign completed degree (M.Sc.level) corresponding to a minimum of four years in the Norwegian educational system Must have documented solid mathematical foundations on optimization (e.g., optimization modelling and numerical optimization), game theory, AI/ML (A focus on explainable AI e.g., will be valuable) Must have documented Background/Knowledge on Energy/Systems/Energy Informatics and edge intelligence etc. Must