On this webpage you’ll find information about the University of Tartu Institute of Computer Science courses that are looking for student teaching assistants. It’s a great opportunity to share your knowledge with fellow students while honing your teaching skills. The positions are paid. If you have any questions about a particular course, contact the course instructor directly.
Web Security (LTAT.04.018)
Contact: Arnis Parsovs, arnis.parsovs@ut.ee
Semester: Spring 2026
Required languages: English
Tasks: implementing semi-automated grading, grading weekly homework submissions
Required skills: have passed the course with good results
Applied Cryptography (MTAT.07.017)
Contact: Arnis Parsovs, arnis.parsovs@ut.ee
Semester: Spring 2026
Required languages: English
Tasks: implementing semi-automated grading, grading weekly homework submissions
Required skills: have passed the course with good results
Artificial Intelligence (LTAT.01.003)
Contact: Krista Liin, krista.liin@ut.ee
Semester: Spring 2026
Required languages: Estonian
Ülesanded: grading homework every 2 weeks, giving feedback to students
Required skills: basic knowledge of Python, AI, NLP. Previous experience with the course is a bonus
Other important information: remote work
Introduction to Data Science (LTAT.02.002)
Contact: Anna Aljanaki, anna.aljanaki@ut.ee
Semester: kevad 2026
Languages of instruction: Eesti
Tasks: 6 praktikumi, kus õpitakse juhendaja toel kasutama Jupyter Notebook keskkonda, teha kirjeldavat analüüsi, läbi viia statistilisi teste, tutvutakse masinõppe, mudelite hindamise ja juurutamisega. Kodutööde hindamine
Required skills: põhiteadmised Pythonist and masinõppest
Other important information: On võimalus valida kahe rühma vahel. Üks rühmades õpib Tartus, teine Narvas. Praktikumid toimuvad neljapäeviti Tartus kl 10 (12.02, 26.02, 12.03, 26.03, 09.04, 23.04) või Narvas erinevatel aegadel (26.02, 19.03, 16.04, 09.05).
Design Thinking and Business Modelling (LTAT.05.040)
Contact: Antti Ainamo, antti.ainamo@ut.ee
Semester: Spring 2026
Languages of instruction: English
Tasks: Course planning with Assoc. Prof. Antti Ainamo taking main responsibility, interaction with students, Moodle, OIS2
Required skills: Moodle, OIS2, English
Computer Security (LTAT.06.002)
Contact: Alo Peets, alo.peets@ut.ee
Semester: Spring 2026
Required languages: Estonian
Tasks: preparing, teachings and grading labs
Required skills: Good communicator, high knowledge about the workings of a computer and operating system
Language Technology (LTAT.01.002)
Contact: Krista Liin, krista.liin@ut.ee
Semester: Autumn 2026
Language: Estonian
Tasks: grading homework every 2 weeks, giving feedback to students
Required skills: basic knowledge of Python, AI, NLP. Previous experience with the course is a bonus.
Other important information: remote work.
Language Technology (LTAT.01.002)
Contact: Krista Liin, krista.liin@ut.ee
Semester: Autumn 2026
Language: Estonian
Tasks: teaching practical sessions once a week, updating teaching materials (labs and homework)
Required skills: basic knowledge of Python, AI, NLP, courage to teach. Previous experience with the course is a bonus.
Other important information: several spots available - think how many groups you are ready to teach the same thing to.
Operating Systems (LTAT.06.001)
Contact: Alo Peets, alo.peets@ut.ee
Semester: Autumn 2026
Required languages: Estonian
Tasks: preparing, teachings and grading labs
Required skills: Good communicator, high knowledge about the workings of a computer and operating system
Business Data Analytics (MTAT.03.319)
Contact: Ahmed Sabir, ahmed.sabir@ut.ee
Semester: Autumn 2026
Required languages: English
Tasks: assist with teaching the lab section
Required skills: Python and basic machine learning algorithms (e.g., logistic regression and K-means)
Other important information: The lab section is online, and all materials have already been prepared
Machine Learning (MTAT.03.227)
Contact: Anna Aljanaki, anna.aljanaki@ut.ee
Semester: Autumn 2026
Language: Estonian
Tasks: teaching practical sessions once in two weeks
Required skills: knowledge of Python and basic machine learning techniques (K-means, PCA, SVM, multi-layer perceptron, scikit-learn), ability to explain techniques in a simple way
Other important information: this is a course taught to master students on the conversion master curriculum. They have a practice session once in two weeks (7 sessions total). Practical materials are available. Preference for someone who is not too far advanced in these topics, as you may be better positioned to explain things simply and recall what was challenging when first learning them.