| Institution | Faculty of Social Sciences - Department of Sociology - Computational Social Sciences |
| Full-time / Part-time | Part-time (8 hours/week) |
| Start date | 2024-12-01 |
| Application deadline | 2024-10-18 |
About us:
You will support us in the ToxicAInment project, where we aim to identify toxic content in entertaining social media data, e.g. TikTok. The project data is multi-modal (videos, images, audio & text) and will be analyzed using classifiers for toxicity based on proprietary and open-source models. More information is available on the following website:
We are looking for you:
Student Assistant for Large-Language and Vision-Language Models (m/f/x)
in Munich
Your tasks and responsibilities:
You will support our research agenda with prompt and model evaluations, model performance metrics, as well as automated and manual data annotations. You will also assist our social scientists in interacting with the data and the models through our own interactive user interface.
Your qualifications:
- Proficient in English and / or German language
- Reliable and able to work independently
- Good command of the programming language Python
- Successfully completed a course in Machine Learning or equivalent fields
- It's a plus to have taken more advanced courses on LLMs, CNNs, or other related topics
- It's a plus to have worked with Python Streamlit, ML frameworks, and REST APIs.
Benefits:
- An Initial duration of 6 months (an extension is both possible and desired)
- Flexible working hours: the workload can be reduced or increased to some extent (please indicate your preferred working hours in your application)
- A great opportunity to gather hands-on experience with state-of-the art applications of AI and LLMs
- The possibility to contribute to research on some of the most pressing issues of our time
People with disabilities who are equally as qualified as other applicants will receive preferential treatment.
Contact:
Have we aroused your interest?
Then apply by sending us your resume, a transcript of records for your completed courses, as well as a paragraph (max. half a page) about why you are a good fit to the position to team.css@soziologie.lmu.de in a single PDF (max. 10MB). Feel free to include links to code samples or machine learning projects you worked on. If you have questions about the position, please reach out to Tomas Ruiz (t.ruiz@lmu.de). We are looking forward to receiving your application up until Friday, October 18.
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