Department of Computer Science
Faculty of 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 Interpretable Natural Language Processing.
Start date is (expected to be) 1st September 2025 or as soon as possible thereafter.
The position The PhD fellowship is offered with
Pepa Atanasova as the primary (de facto) supervisor, and co-supervised with
Isabelle Augenstein, who serves as the formally designated principal supervisor. The successful candidate will engage in developing innovative methods for interpreting and explaining the inner workings of natural language processing models. The research will broadly align with the
main interests of primary supervisor and the objectives of the
CopeNLU group. It may include exploring the mechanisms by which language models process and represent knowledge, devising methods to provide reliable explanations for models' decision-making processes, or developing techniques to ensure accountability in model’s behavior.
While the overall focus of the PhD will be within the area of NLP interpretability and explainability, the specific research direction will be tailored in collaboration with the candidate to align with their interests and the group's research goals. The candidate will join a vibrant research team that includes collaborators from CopeNLU and other international partners, fostering a dynamic and interdisciplinary environment conducive to impactful research.
Who are we looking for? Applicants should hold a MSc degree or equivalent in Computer Science or a related field, and have good written and oral English skills. The assessment of your qualifications will also be made based on previous scientific publications (if any) and relevant work experience. The ideal candidate would have an education background, prior research or work experience in ML or NLP.
Our group and research- and what do we offer? The successful candidate will join the
CopeNLU group at the University of Copenhagen. CopeNLU is a vibrant and collaborative research group led by Isabelle Augenstein and Pepa Atanasova with a focus on researching methods for tasks that require a deep understanding of language, as opposed to shallow processing. We are interested in core methodology research on, among others, learning with limited training data, interpretable and explainable AI; as well as applications thereof to tasks such as fact checking, gender bias detection and question answering. With a strong focus on both foundational and applied research, we provide a platform for exploring cutting-edge topics in NLP, while also emphasizing the importance of transparent and responsible AI development.
We are affiliated with the
Natural Language Processing Section in the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen, as well as with the
Pioneer Centre for AI, at the Department of Computer Science, University of Copenhagen. The group is currently co-located with the Pioneer Centre for AI in central Copenhagen. The Natural Language Processing Section provides a strong, international and diverse environment for research within core as well as emerging topics in natural language processing. The Natural Language Processing research environment at the University of Copenhagen is internationally leading, as e.g. evidenced by it being ranked top-5 in Europe according to CSRankings. Further information about research at the Department is available here:
https://di.ku.dk/english/research/ .
Principal supervisor is
Prof. Isabelle Augenstein, Department of Computer Science, email: [email protected] The PhD student will be co-supervised by Pepa Atanasova, email: [email protected] . The PhD programme You can undertake the PhD programme as:
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. Getting into a position on the regular PhD programme Qualifications needed for the regular programme To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the position, i.e. NLP and ML. For information of eligibility of completed programmes, see
General assessments for specific countries and
Assessment database.
Terms of employment in the regular programme Employment as PhD fellow is full time and for maximum 3 years.
Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific position formulated by the applicant.
Terms of appointment and payment accord to the agreement between the Danish Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. The position is covered by the Protocol on Job Structure.
Responsibilities and tasks in the PhD programme - Carry through an independent research project under supervision
- Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
- Participate in active research environments, including a stay at another research institution, preferably abroad
- Teaching and knowledge dissemination activities
- Write scientific papers aimed at high-impact journals
- Write and defend a PhD thesis on the basis of your project
We are looking for the following qualifications: - Professional qualifications relevant to the PhD position
- Relevant publications
- Relevant work experience
- Other relevant professional activities
- Curious mind-set with a strong interest in Interpretable Natural Language Processing
- Good language skills
Application and Assessment Procedure Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
Please include:
- Cover Letter detailing your motivation and background for applying for this PhD position.
- Research Statement detailing your desired research focus and goals for the PhD studies within the scope of the specified position.
- Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position;
- 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.
- Publication list (if possible);
- Reference letters (if available);
Application deadline: The deadline for applications is
15th January 2025, 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 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
http://employment.ku.dk/faculty/recruitment-process/.
Interviews with selected candidates are expected to be held between 3rd -15th February 2025.
Questions For specific information about the PhD fellowship, please contact
[email protected] .
General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website:
https://www.science.ku.dk/phd/.
The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.