This is the page describing the lab called “Software Development Tools (SDT)”, and also theses around this topic.
Each semester a few students can participate in a lab or a thesis. Usually, there is no list of open topics, each topics is aranged individually by discussion between the student and a researcher of the Software Technology Group. Labs and theses differ in the workload and credit points you get:
| what | credit | workload |
|---|---|---|
| lab | 06cp | 08 hours/week |
| bachelor thesis | 12cp | 16 hours/week |
| master thesis | 30cp | 40 hours/week |
⚠️ Note: Students occassionally underestimate the workload of a lab or thesis. In particular, a master thesis is not something you can do on the side! If you plan to do a lab, you should dedidacte a full day each week for it, for a bachelor thesis two days, and for a master thesis five days.
How to contact us, so that you get a response
Look at our list of research interest below and contact a few (but not all) persons by email, if you are interested in pursuing a topic related to their research interests for your lab or thesis.
In your email, mention:
- First make clear what you are looking for, and what lead you to this person, for example:
- “I am contacting you because you are mentioned on website XXX in a list of person to contact for a lab / bachelor thesis / master thesis. Of the topics mentioned for you, YYY catched my interest.”).
- Programming Languages and other Skills:
- the programming languages you speak (and are interested in learning), and
- other (programming) experiences and skills that are relevant to the topic
- Relevant Courses:
- if and which relevant courses you have visited
- in particular our courses, but other courses may also be relevant
- Seminar: Artificial Intelligence for Coding Assistance (AI4CA)
- Transcript of Records: a pdf export of your grades in tucan
Finally, if you are not sure who to contact or if you did not receive a response in 7 days, write a mail to jobs@stg.tu-….
Who to Contact, and their Research Interests
- Code Models
- Training and Fine-Tuning of Code Models
- Evaluation and Benchmarking of Code Models
- Embeddings and Representation Learning
- Exploring New Model Architectures: Transformer Models, Graph Models, State-Space Models
- Shweta Verma
- Abhinav Anand
- Mert Tiftikci
- Daniel Maninger
- Various
- Dr. Amir Molzam Sharifloo – Artificial Intelligence for Software Engineering
- Dr. Tobias Reinhard – Neurosymbolic Reasoning and Program Verification
- Jannis Brugger – Symbolic Regression, Automated Scientists (Machine Learning for Physics Experiments)
- Dr. Isabella Graßl – Education, Diversity, Teamwork, Society, Creativity, and Design in Computing