项目介绍

2026年7月9日

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 have documented significant Knowledge/Research Background, or Must be able to demonstrate skills on Data Analytics and Machine Learning, in particular on distributed ML.
• Must have very good programming competence in Python, Java, C/C++ or equivalent
• Fluent oral and written communication skills in English

Desired qualifications:

• Publications in reputed journals and conferences in the field
• Excellent presentation skills
• Experience working in an cross-disciplinary/International Team

Language requirement:

• Good oral and written communication skills in English
• English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements:
  https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8

Grade requirements:
The norm is as follows:

• The average grade point for courses included in the Bachelor’s degree must be C or better in the Norwegian educational system
• The average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational system
• The Master’s thesis must have the grade B or better in the Norwegian educational system

For more information see:

The purpose of the fellowship is research training leading to the successful completion of a PhD degree. 

All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation, the Sanctions regulation, and the national security regulation.

What are we looking for in you?

Personal skills:

• Ability to work both independently and as part of a team
• Ability to work precise in a structured manner and swiftly adapts to new tasks
• Good communication and collaboration skills
• Positive attitude and the ability to handle hectic periods

The candidate should demonstrate:

• strong motivation, curiosity, proactiveness and a learning-oriented mindset
• capability to work independently, take initiative, and maintain good structure and discipline in their work
• communicate effectively and collaborate well with supervisors and peers
• resilience and work constructively when facing challenges or setbacks
• integrity and a strong sense of responsibility in their research conduct
• high interest and motivation for collaboration with industrial and interdisciplinary teams
• ability to write in a structured, clear and concise manner

The evaluation considers many aspects of excellence, as well as the personal drive and organizational skills. The candidate should also possess good interpersonal and communication skills and show high level of motivation to work as part of an international team.
Employment in the position is based on a comprehensive assessment of all qualification requirements applicable to the position, including personal qualifications.

We can offer you

• A pleasant and stimulating work environment
• Good welfare schemes
• Opportunity of up to 1.5 hours a week of exercise during working hours
• A workplace with good development and career opportunities
• Career development programmes
• Membership in the Statens Pensjonskasse, which is one of Norway’s best pension schemes with beneficial mortgages and good insurance schemes
• Oslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activities
• Salary in position as PhD Research Fellow, position code 1017 in salary range NOK from 550 800 – 595 000, depending on competence and experience. From the salary, 2 percent is deducted in statutory contributions to the State Pension Fund 

We need different perspectives in our work

UiO is an open and internationally oriented comprehensive university that strives to be an inclusive and diverse workplace and academic environment. You can read more about UiO’s work on equality, inclusion, and diversity at uio.no.

We fulfill our mission most effectively when we draw upon our variety of experiences, backgrounds, and perspectives. We are looking for great colleagues, could you be the next one?

We will do our best to accommodate your needs. Relevant adjustments may include modifications to working hours, task adaptations, digital, technical, or physical adjustments, or other practical measures.

If you have an immigrant background, a disability, or CV gaps (Norwegian), we encourage you to indicate this in the job application portal. We always invite at least one qualified candidate from each group for an interview. In this context, disability is defined as an applicant who identifies as having a disability that requires workplace or employment-related accommodations. For more details about the requirements, please refer to the Employer portal (Norwegian).

The selections made in the job application portal are used for anonymized statistics that all state employers include in their annual reports. More information about gender equality initiatives at UiO can be found here. 

We hope you will apply for the position with us.

How to apply

The application must include:

• Cover letter – statement of motivation and research interests
• CV (summarizing education, positions and academic work – scientific publications)
• Copies of the original Bachelor and Master’s degree diploma and transcripts of records
• Three letters of recommendation
• Names and contact details of three references (name, relation to candidate, e-mail and telephone number)
• Documentation of English proficiency if applicable
• List of publications and academic work that the applicant wishes to be considered by the evaluation committee