PhD Fellowship in Computer Science (University of Copenhagen)

Department: Department of Computer Science Faculty: SCIENCE, University of Copenhagen The Natural Language Processing Section at the Department of Computer Science, Faculty of Science at the University of Copenhagen invites applicants for a PhD fellowship in Explainable Natural Language Understanding. Start date: Expected 1 March 2026 or as soon as possible thereafter. The Position The PhD fellowship is offered in the context of an ERC Starting Grant held by Isabelle Augenstein on ‘Explainable and Robust Automatic Fact Checking (ExplainYourself)’. The ERC Starting Grant is a highly competitive funding program by the European Research Council to support talented early-career scientists in Europe with funding for a period of 5 years to build up or expand their research groups. ExplainYourself proposes to study explainable automatic fact checking, the task of automatically predicting the veracity of textual claims using machine learning methods while also producing explanations of how the model arrived at the prediction. Existing methods often use opaque deep neural network models, which hinder user trust. ExplainYourself proposes methods for explainable fact checking that more accurately reflect how models make decisions, while also being useful to diverse groups of end users. The project team currently consists of the principal investigator, three PhD students, two postdocs, collaborators from CopeNLU, and external collaborators. The recruited PhD student is expected to define their project within the overall project scope and collaborate with the larger project team. Whom Are We Looking For? Applicants should hold an MSc degree or equivalent in Computer Science or a related field, and have good written and oral English skills. Assessment will also consider previous scientific publications (if any) and relevant work experience. Ideal candidate background: Education, prior research, or work experience in Machine Learning (ML) or Natural Language Processing (NLP). Our Group and What We Offer The successful candidate will join the CopeNLU group at the University of Copenhagen, led by Isabelle Augenstein and Pepa Atanasova. The group focuses on methods for deep language understanding, interpretable and explainable AI, and applications to tasks such as fact checking and question answering. We are affiliated with: The Natural Language Processing Section, Department of Computer Science, Faculty of SCIENCE, University of Copenhagen The Pioneer Centre for AI, Department of Computer Science, University of Copenhagen The group emphasizes foundational and applied research in NLP while promoting transparent and responsible AI development. Principal Supervisor: Prof. Isabelle Augenstein – augenstein@di.ku.dk Co-Supervisor: Pepa Atanasova – pepa@di.ku.dk The PhD Programme Study Framework: Three-year full-time study within the regular PhD programme (5+3 scheme), for candidates with a degree equivalent to a relevant Danish Master’s. Eligibility: Completion of a degree programme equivalent to a Danish Master’s (180 ECTS BSc + 120 ECTS MSc) in NLP, ML, or related areas. Terms of Employment: Full-time, on-site in Copenhagen, maximum 3 years. Conditional upon successful enrolment at the PhD School, Faculty of SCIENCE, University of Copenhagen. Salary and terms follow the Danish Ministry of Taxation and the Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure. Responsibilities and Tasks Conduct a research project with increasing autonomy from supervisors. Collaborate within the research group and with external project partners. Author scientific papers aimed at high-impact venues. Disseminate research nationally and internationally. Attend PhD courses (general skills and specialized courses in ML/NLP). Conduct a change of scientific environment, preferably abroad. Write and defend a PhD thesis based on the project. Qualifications Professional qualifications relevant to the PhD position. Relevant educational background. Relevant publications and work experience. Curious mindset with a strong interest in explainable NLU. Good written and oral English language skills. Application and Assessment Procedure Applications must be in English and submitted electronically. Required Documents: Cover Letter detailing motivation and background. Research Statement outlining desired research focus, specific research questions, methodological approaches, connections to existing literature, and potential impact. Curriculum vitae including education, experience, language skills, and other relevant skills. Original diplomas for Bachelor’s and Master’s degrees and transcript of records (authorized English translation if needed). If not completed, a certified copy of recent transcript or written statement from institution/supervisor is accepted. Publication list (if available). Reference letters (if available; alternatively, reference letters can be submitted directly to augenstein@di.ku.dk for shortlisted candidates, within one week of shortlisting). Application Deadline: 31 October 2025, 23:59 GMT +1 Note: Late applications or incomplete submissions will not be considered. Selection Process Selected applicants undergo academic assessment by unbiased expert assessors. Candidates will be notified about assessment outcomes. Assessors evaluate qualifications and experience, and applicants can comment on the assessment. Interviews are expected between 13 – 28 November 2025. Contact For specific information about the PhD fellowship: augenstein@di.ku.dk General information about PhD study at the Faculty of SCIENCE is available at the PhD School. Application Start Date: 1 March 2026 Department/Location: Department of Computer Science, University of Copenhagen The University of Copenhagen encourages all applicants regardless of personal background and promotes research and teaching of the highest international standard in an ambitious and informal environment. Contact: Isabelle Augenstein – augenstein@di.ku.dk Pepa Kostadinova Atanasova – pepa@di.ku.dk Application Deadline: 31-10-2025 If you apply for this position please say you saw it on Computeroxy

Department: Department of Computer Science
Faculty: SCIENCE, University of Copenhagen

The Natural Language Processing Section at the Department of Computer Science, Faculty of Science at the University of Copenhagen invites applicants for a PhD fellowship in Explainable Natural Language Understanding.

Start date: Expected 1 March 2026 or as soon as possible thereafter.

The Position

The PhD fellowship is offered in the context of an ERC Starting Grant held by Isabelle Augenstein on ‘Explainable and Robust Automatic Fact Checking (ExplainYourself)’. The ERC Starting Grant is a highly competitive funding program by the European Research Council to support talented early-career scientists in Europe with funding for a period of 5 years to build up or expand their research groups.

ExplainYourself proposes to study explainable automatic fact checking, the task of automatically predicting the veracity of textual claims using machine learning methods while also producing explanations of how the model arrived at the prediction. Existing methods often use opaque deep neural network models, which hinder user trust. ExplainYourself proposes methods for explainable fact checking that more accurately reflect how models make decisions, while also being useful to diverse groups of end users.

The project team currently consists of the principal investigator, three PhD students, two postdocs, collaborators from CopeNLU, and external collaborators. The recruited PhD student is expected to define their project within the overall project scope and collaborate with the larger project team.

Whom Are We Looking For?

Applicants should hold an MSc degree or equivalent in Computer Science or a related field, and have good written and oral English skills. Assessment will also consider previous scientific publications (if any) and relevant work experience.

Ideal candidate background:

Education, prior research, or work experience in Machine Learning (ML) or Natural Language Processing (NLP).

Our Group and What We Offer

The successful candidate will join the CopeNLU group at the University of Copenhagen, led by Isabelle Augenstein and Pepa Atanasova. The group focuses on methods for deep language understanding, interpretable and explainable AI, and applications to tasks such as fact checking and question answering.

We are affiliated with:

The Natural Language Processing Section, Department of Computer Science, Faculty of SCIENCE, University of Copenhagen
The Pioneer Centre for AI, Department of Computer Science, University of Copenhagen

The group emphasizes foundational and applied research in NLP while promoting transparent and responsible AI development.

Principal Supervisor: Prof. Isabelle Augenstein – augenstein@di.ku.dk
Co-Supervisor: Pepa Atanasova – pepa@di.ku.dk

The PhD Programme

Study Framework: Three-year full-time study within the regular PhD programme (5+3 scheme), for candidates with a degree equivalent to a relevant Danish Master’s.

Eligibility: Completion of a degree programme equivalent to a Danish Master’s (180 ECTS BSc + 120 ECTS MSc) in NLP, ML, or related areas.

Terms of Employment:

Full-time, on-site in Copenhagen, maximum 3 years.
Conditional upon successful enrolment at the PhD School, Faculty of SCIENCE, University of Copenhagen.
Salary and terms follow the Danish Ministry of Taxation and the Danish Confederation of Professional Associations on Academics in the State (AC).
The position is covered by the Protocol on Job Structure.

Responsibilities and Tasks

Conduct a research project with increasing autonomy from supervisors.
Collaborate within the research group and with external project partners.
Author scientific papers aimed at high-impact venues.
Disseminate research nationally and internationally.
Attend PhD courses (general skills and specialized courses in ML/NLP).
Conduct a change of scientific environment, preferably abroad.
Write and defend a PhD thesis based on the project.

Qualifications

Professional qualifications relevant to the PhD position.
Relevant educational background.
Relevant publications and work experience.
Curious mindset with a strong interest in explainable NLU.
Good written and oral English language skills.

Application and Assessment Procedure

Applications must be in English and submitted electronically.

Required Documents:

Cover Letter detailing motivation and background.
Research Statement outlining desired research focus, specific research questions, methodological approaches, connections to existing literature, and potential impact.
Curriculum vitae including education, experience, language skills, and other relevant skills.
Original diplomas for Bachelor’s and Master’s degrees and transcript of records (authorized English translation if needed). If not completed, a certified copy of recent transcript or written statement from institution/supervisor is accepted.
Publication list (if available).
Reference letters (if available; alternatively, reference letters can be submitted directly to augenstein@di.ku.dk for shortlisted candidates, within one week of shortlisting).

Application Deadline: 31 October 2025, 23:59 GMT +1

Note: Late applications or incomplete submissions will not be considered.

Selection Process

Selected applicants undergo academic assessment by unbiased expert assessors.
Candidates will be notified about assessment outcomes.
Assessors evaluate qualifications and experience, and applicants can comment on the assessment.
Interviews are expected between 13 – 28 November 2025.

Contact

For specific information about the PhD fellowship: augenstein@di.ku.dk

General information about PhD study at the Faculty of SCIENCE is available at the PhD School.

Application Start Date: 1 March 2026
Department/Location: Department of Computer Science, University of Copenhagen

The University of Copenhagen encourages all applicants regardless of personal background and promotes research and teaching of the highest international standard in an ambitious and informal environment.

Contact: