Technische Universität Dresden
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PhD and Postdoc Positions in LLMs, NLP, Knowledge Graphs, and Graph Machine Learning at TU Dresden Technische Universität Dresden in Germany
Degree Level
PhD, Postdoc
Field of study
Computer Science
Funding
Full-time positions with TV-L E13 salary. No stipend amount is stated; the post indicates paid employment rather than a scholarship.
Deadline
Sep 15, 2026
Country
Germany
University
Technische Universität Dresden

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About this position
Technische Universität Dresden / ScaDS.AI Dresden/Leipzig is advertising PhD and postdoc positions in large language models (LLMs), natural language processing (NLP), knowledge graphs, and graph machine learning.
The roles are based in Germany and are described as full-time TV-L E13 positions. The research group highlights a strong publication culture and encourages candidates who want to develop their own research ideas and publish in high-quality international journals.
Research themes include developing new methods for LLMs and NLP, working with knowledge graphs, and advancing graph ML approaches. The post also mentions participation in international conferences and workshops to present results.
Eligibility/preferences: strong interest in independent research, publication-oriented work, and experience or motivation in AI/ML-related topics. The post does not list detailed degree or language requirements.
Deadline: 2026-09-15.
How to apply: Apply online via the TU Dresden posting linked in the announcement. Review the PDF carefully and submit before the deadline.
Funding details
Full-time positions with TV-L E13 salary. No stipend amount is stated; the post indicates paid employment rather than a scholarship.
What's required
Candidates should have a strong interest in developing their own research ideas and publishing in high-quality international journals. The post is aimed at a PhD student and a postdoc with research interests in LLMs, NLP, knowledge graphs, and graph machine learning.
How to apply
Apply online using the linked TU Dresden application PDF/portal. Review the posting details and submit the application through the university's online process before the deadline.
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