Fully funded PhD fellowship in Resource Efficiency for Generative AI Full-time

PhD Fellowship In Resource Efficient And Sustainable Generative AI

The Inference & Retrieval Lab within the Department of Computer Science (DIKU) at the University of Copenhagen invites applications for a fully funded PhD Fellowship commencing in January 2027, or as soon as possible thereafter.

The successful candidate will undertake research on Resource Efficiency for Generative AI, such as Large Language Models (LLMs) and AI agents, contributing to the development of more efficient, sustainable, and accessible AI systems.

Join a vibrant and internationally recognised research environment

The PhD fellow will become a member of the Inference & Retrieval Lab, one of the research labs within the Machine Learning Section at DIKU. The Machine Learning Section is an internationally recognised research environment with a strong track record of excellence in the broader area of Machine Learning, including Natural Language Processing and Web & Information Retrieval. According to CSRankings, the section has consistently ranked among the top research environments in Europe, including within the top 3 in Natural Language Processing and top 6 in Web & Information Retrieval over the past five years.

Our research community is characterised by a strong presence at leading international conferences, active participation in national and international research networks, and close collaborations with large technology companies, innovative start-ups, and industry partners. The section brings together approximately 65 researchers from around the world, including around 40 PhD fellows and postdoctoral researchers, representing diverse academic and cultural backgrounds.

What unites us is a shared commitment to scientific excellence, a strong sense of curiosity, and an openness to new ideas, perspectives, and approaches. As a PhD fellow, you will become part of a collaborative and intellectually stimulating environment in which you will have the opportunity to develop your research profile, engage with an international research community, and contribute to cutting-edge advances in AI.

Research focus: Resource efficient and sustainable Generative AI

The PhD project will broadly explore resource efficient Generative AI, such as Large Language Models and AI agents, and their role in the sustainability of AI. The research may address resource efficiency at different stages of the LLM lifecycle, including the development of novel algorithms, training and learning paradigms, prompting and inference strategies, and hardware-aware optimisation techniques. The overarching goal is to investigate approaches that can substantially reduce the computational, energy, and other resources required to develop and deploy LLM-based systems, while maintaining or improving their performance and capabilities. The project may also explore the broader relationship between resource efficiency and the sustainability of Generative AI, including dimensions such as safety, fairness, or accessibility.

The successful candidate will be expected to formulate and develop an independent and ambitious PhD research project within this broad area, in close collaboration with the supervisory team.

Supervision and collaboration

The PhD fellow will be supervised by:

• Professor Christina Lioma — Principal Supervisor
• Associate Professor Maria Maistro
• Assistant Professor Raghavendra Selvan

The supervisory team brings complementary expertise across machine learning, natural language processing, information retrieval, and efficient AI, providing the candidate with a strong foundation for pursuing interdisciplinary and impactful research.

We warmly encourage prospective candidates who are passionate about efficient, sustainable, and responsible AI and who wish to contribute to the next generation of Large Language Models to apply.

For further information about the position or the research project, prospective applicants are welcome to contact Professor Christina Lioma, Associate Professor Maria Maistro, or Assistant Professor Raghavendra Selvan.

The University of Copenhagen

The University of Copenhagen was founded in 1479 and is the oldest and largest institution of research and education in Denmark. It is a member of the International Alliance of Research Universities, alongside the Universities of Cambridge, Oxford and Yale, and has produced 10 Nobel prize winners. Various academic rankings see the University of Copenhagen as one of the top leading institutions in Europe and the world, and its study programs meet the most stringent international standards for higher education based on Standards and Guidelines for Quality Assurances in the European Higher Education Area and the Danish Accreditation Institution guidelines.

Who are we looking for?

We are looking for candidates with a MSc degree in a subject relevant for the research area. The successful candidate is expected to have strong grades in Machine Learning and/or Natural Language Processing and/or Information Retrieval and/or Recommender Systems. Successful candidates should have a) fluency in spoken and written English, b) strong academic writing skills, and c) a preliminary research record as witnessed by a master thesis or publications in the area. As for any research position in this area, successful candidates are expected to have scientific curiosity, critical thinking skills, and strong programming skills.

The PhD programme

Depending of your level of education, you can undertake the PhD programme as either:

Option A: A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master's degree.

Option B: An up to five year full-time study programme within the framework of the integrated MSc and PhD programme (the 3+5 scheme), if you do not have an education equivalent to a relevant Danish master’s degree – but you have an education equivalent to a Danish bachelor’s degree.

Responsibilities and tasks in both PhD programmes

• Complete and pass the MSc education in accordance with the curriculum of the MSc programme
• Carry through an independent research project under supervision
• Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
• Carry out limited dissemination and teaching activities
• Participate in active research environments, including a stay at another research institution, preferably abroad
• Write scientific papers aimed at high-impact venues
• Write and defend a PhD thesis on the basis of your project

We are looking for the following qualifications:

• A minimum grade point average of 80% or equivalent
• Relevant publications are desirable, but not necessary
• Relevant work experience is desirable, but not necessary
• Curious mind-set with a strong interest in Machine Learning and/or Information Retrieval and/or Recommender systems and/or Natural Language Processing
• Proficiency in the English language

Application and Assessment Procedure

Your application including all attachments must be in English and submitted electronically.

Please include:

1. Letter of application (max. one page) describing the research topic you wish to pursue.
2. Curriculum vitae including information about your education, experience, language skills, programming skills, and other skills relevant for the position.
3. The names and contact details of two referees who have agreed to be contacted and give references.
4. Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
5. Publication list (if possible).

Application deadline: 10 October 2026, 23:59 CET

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process

After the deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.

The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.

Questions

For specific information about the PhD fellowship, please contact the principal supervisor.

General information about PhD study at the Faculty of SCIENCE is available at the PhD School's website: https://www.science.ku.dk/phd/.