Would you like to join our lab as a PhD student in 2026? We have several openings. Read more about reasons to join CopeNLU here.
Start in Spring 2026
A fully funded 3-year PhD fellowship on explainable natural language understanding for a start in Spring 2026 is available as part of the ExplainYourself project on Explainable and Robust Automatic Fact Checking. The position requires candidates to have completed a Master’s degree by the start date. The successful candidate will be supervised by Isabelle Augenstein and co-supervised by Pepa Atanasova. Read more about the position and apply here by 31 October 2025.
The project is funded by an ERC Starting Grant, a highly competitive funding program by the European Research Council, which supports the most talented early-career scientists in Europe with funding for a period of 5 years for blue-skies research 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 (ML) methods, while also producing explanations about how the model arrived at the prediction. Automatic fact checking methods often use opaque deep neural network models, whose inner workings cannot easily be explained. Especially for complex tasks such as automatic fact checking, this hinders greater adoption, as it is unclear to users when the models’ predictions can be trusted. Existing explainable ML methods partly overcome this by reducing the task of explanation generation to highlighting the right rationale. While a good first step, this does not fully explain how a ML model arrived at a prediction. For knowledge intensive natural language understanding (NLU) tasks such as fact checking, a ML model needs to learn complex relationships between the claim, multiple evidence documents, and common sense knowledge in addition to retrieving the right evidence. There is currently no explainability method that aims to illuminate this highly complex process. In addition, existing approaches are unable to produce diverse explanations, geared towards users with different information needs. ExplainYourself radically departs from existing work in proposing methods for explainable fact checking that more accurately reflect how fact checking models make decisions, and are useful to diverse groups of end users. It is expected that these innovations will apply to explanation generation for other knowledge-intensive NLU tasks, such as question answering or entity linking.
In addition to the principal investigator, PhD students and postdocs, the project team includes collaborators from CopeNLU as well as external collaborators. Three PhD students as well as two postdocs have already been recruited as a result of earlier calls, and the project officially kicked off in September 2023.
Start in Autumn 2026
For a start in Autumn 2026, we are considering candidates on any topic aligned with the focus areas of our lab. Candidates should express their interest by applying to the ELLIS PhD programme by 31 October 2025, naming Isabelle Augenstein as a supervisor. ELLIS is a pan-European recruitment vehicle for PhD students and does not provide funded PhD fellowships, though it offers networking opportunities, and opportunities to obtain travel funding.
Successful candidates will be supported in applying for funded fellowship opportunities, including those offered by the Danish Data Science Academy (DDSA) and the Danish Advanced Research Academy (DARA). Additional fully funded PhD positions may become available through the Pioneer Centre for AI. Candidates without Master’s degrees may be eligible, depending on the conditions of the respective fellowship.