{"id":880,"date":"2026-09-03T12:29:27","date_gmt":"2026-09-03T09:29:27","guid":{"rendered":"https:\/\/sisu.ut.ee\/digits\/?page_id=880"},"modified":"2026-10-07T11:59:06","modified_gmt":"2026-10-07T08:59:06","slug":"join-the-team","status":"publish","type":"page","link":"https:\/\/sisu.ut.ee\/digits\/join-the-team\/","title":{"rendered":"Join our team!"},"content":{"rendered":"

<\/p>\n\n\n\n

Are you looking for a new challenge?
<\/h2>\n\n\n\n

<\/p>\n\n\n\n

The call for applications has now been closed. Suitable candidates will be notified about interview times by October 10 the latest. Interviews will take place online on October 13.<\/p>\n\n\n\n

\n\n\n\n

<\/p>\n\n\n\n

The DigiTS team is looking for a junior researcher to join our team to work on linguistic annotation with LLM-s.<\/p>\n\n\n\n

\"\"<\/figure>
\n

This specialisation belongs to the doctoral programme in Humanities<\/a> at the University of Tartu <\/a>(Estonia).<\/p>\n\n\n\n

There is 1 position<\/strong> available. <\/p>\n\n\n\n

Research topic open for admission<\/strong>: Linguistic Annotation with LLMs (supervisor Maciej Eder)
It is not possible to apply with your own topic during the application period.<\/p>\n\n\n\n

Description of the topic<\/strong>:<\/p>\n\n\n\n

The PhD project is aimed at testing several Large Language Models in solving a variety of different linguistic annotation tasks. Intuitively, some of these tasks should be accomplished by the models at an acceptable accuracy level, but we have no empirical evidence to support such a hypothesis. This project will try to test, in a series of carefully designed experiments, the extent to which LLMs are capable of recognizing patterns in scenarios that they were not optimized for.<\/p>\n\n\n\n

Primarily, the datasets used in this project will be already-existing manually-annotated \u201cgold sets\u201d from a number of previous studies. Some of the chosen datasets will be relatively easy to solve by most LLMs, whereas some other tasks will be chosen specifically to make the problem difficult for the models.<\/p>\n\n\n\n

The primary methodological setup used in this project will be a series of supervised machine-learning scenarios, in which the same set will be automatically annotated, and then compared against the human-annotated \u201cgold set\u201d. A series of different models will be tested. Distilled and quantized Large Language Models will be used, adjusted through zero-shot prompting. A series of alternative prompting strategies will be conducted. Also, the novel class of reasoning models, implementing chain-of-thought output and imitating human reasoning, will be heavily tested in this project.<\/p>\n\n\n\n

Admitted doctoral researcher will work as a junior research fellow<\/strong> at the university. The estimated workload is 1.0 and the estimated time period is four years. Studies are expected to start between November 2026-February 2027.<\/strong> The final workload and exact starting date will be fixed after the student is admitted.<\/p>\n<\/div><\/div>\n\n\n\n

Candidates must submit an application with other requested documents<\/strong>:<\/p>\n\n\n\n