Fully Funded PhD: Resource-Efficient Generative AI

KU - SCIENCE - DATALOGISK INSTITUT - UP1; København; Presencial; DKK 323.000 - 387.000; Jornada completa

Descripción de la vacante

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 PhD project focuses on Resource Efficient Generative AI, including LLMs and AI agents, with goals to develop sustainable, accessible AI systems.

You will join a diverse research community and work under a supervisory team with strengths spanning ML, NLP, information

Formación

• MSc degree in a subject relevant for the research area.
• Strong grades in Machine Learning and/or Natural Language Processing and/or Information Retrieval and/or Recommender Systems.
• Fluency in spoken and written English; strong academic writing skills.
• Preliminary research record evidenced by a master thesis or publications.

Responsabilidades

• Carry through an independent research project under supervision.
• Develop a research plan and contribute to dissemination through papers and talks.
• Submit a PhD thesis and defend it; participate in dissemination and teaching activities as appropriate.
• Collaborate with the supervisory team and related research groups or industry partners.

Conocimientos

Machine Learning

Natural Language Processing

Information Retrieval

Programming

English proficiency

Educación

Master's degree (MSc)

Descripción del empleo

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 PhD project focuses on Resource Efficient Generative AI, including LLMs and AI agents, with goals to develop sustainable, accessible AI systems.

You will join a diverse research community and work under a supervisory team with strengths spanning ML, NLP, information