Publisher
source

University of Copenhagen

Fully funded PhD Fellowship in Resource Efficiency for Generative AI University of Copenhagen in Denmark

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD fellowship. For the regular PhD programme, employment is full time for up to 3 years with salary starting at DKK 31,800 / 4,200 per month (August 2026 level). For the integrated MSc and PhD programme, funding includes 48 SU-klip plus salary for work until the MSc is obtained, then salary-earning employment for two years; salary starts at DKK 31,800 / 4,200 per month (August 2026 level).

Deadline

Oct 10, 2026

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Country

Denmark

University

University of Copenhagen

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Keywords

Computer Science
Information Technology
Mathematics
Information Retrieval
Natural Language Processing
Resource Efficiency
Statistics
Recommender System
Large Language Models
ML

Suggested positions

About this position

Fully funded PhD Fellowship in Resource Efficiency for Generative AI at the University of Copenhagen, Department of Computer Science (DIKU), in the Inference & Retrieval Lab within the Machine Learning Section.

The project focuses on resource efficient and sustainable Generative AI, including Large Language Models (LLMs) and AI agents. Research directions may include novel algorithms, training and learning paradigms, prompting and inference strategies, hardware-aware optimisation, and broader questions around sustainability, safety, fairness, and accessibility in AI.

The position is part of a strong international research environment with expertise in Machine Learning, Natural Language Processing, Information Retrieval, and related areas. The supervisory team is led by Professor Christina Lioma, with Associate Professor Maria Maistro and Assistant Professor Raghavendra Selvan.

This is a fully funded PhD. For the regular PhD route, the appointment is full time for up to 3 years and includes a salary starting at DKK 31,800 / 4,200 per month (August 2026 level). An integrated MSc and PhD route is also available for eligible candidates, with funding that includes 48 SU-klip plus salary for work until the MSc is completed, followed by salary-earning employment for two years.

Applicants should have a relevant MSc degree, or be eligible for the integrated MSc and PhD programme with a relevant Danish bachelor’s degree. The post asks for strong grades in Machine Learning and/or NLP and/or Information Retrieval and/or Recommender Systems, a minimum GPA of 80% or equivalent, fluent English, strong academic writing, strong programming skills, and preferably a master thesis or publications in the area.

The application deadline is 10 October 2026, 23:59 CET. Apply online through the university portal and include a one-page research proposal, CV, referees, diplomas/transcripts, and publication list if available.

Funding details

Fully funded PhD fellowship. For the regular PhD programme, employment is full time for up to 3 years with salary starting at DKK 31,800 / 4,200 per month (August 2026 level). For the integrated MSc and PhD programme, funding includes 48 SU-klip plus salary for work until the MSc is obtained, then salary-earning employment for two years; salary starts at DKK 31,800 / 4,200 per month (August 2026 level).

What's required

Applicants should have a relevant MSc degree, or be eligible for the integrated MSc and PhD programme with a relevant Danish bachelor’s degree. A minimum GPA of 80% or equivalent is required. Strong grades in Machine Learning and/or Natural Language Processing and/or Information Retrieval and/or Recommender Systems are expected. Candidates should have fluent spoken and written English, strong academic writing skills, scientific curiosity, critical thinking, strong programming skills, and preferably a preliminary research record such as a master thesis or publications. For the integrated programme, applicants must be enrolled in or eligible for one of the faculty’s master’s programmes in Computer Science.

How to apply

Prepare an English application with a one-page research statement, CV, two referees, diplomas/transcripts, and publication list if available. Submit electronically via the APPLY NOW portal before the deadline. Contact the principal supervisor for specific questions about the fellowship.

More information can be found here

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