The Chair of Human-Centered Technologies for Learning at the Technical University of Munich (TUM) offers exciting Ph.D. opportunities focused on the cutting-edge field of Human-Centered AI Technologies using advanced Generative AI and novel interaction technologies.

Potential Ph.D. Projects

• Domain-specific fine-tuning and alignment to create AI systems that are powerful, equitable, trustworthy, and context-sensitive.
• Multi-agent architectures where multiple AI systems collaborate, negotiate, and adapt to model complex human learning processes and support group work.
• Interactive, adaptive learning technologies that integrate multimodal input (text, speech, gesture, eye-tracking) to provide personalized, responsive feedback.
• Human-AI co-adaptation: designing systems where humans and AI learn from and with each other, fostering curiosity, creativity, and deeper understanding.

Who We Are Looking For

We seek highly motivated and talented individuals passionate about AI, LLMs, Human-Computer Interaction, and their responsible applications. Ideal candidates will have:

• An M.Sc. degree (or equivalent) in Computer Science, Game Engineering, Mathematics, Statistics, or related fields.
• Strong programming skills in Python, Java, C++, etc.
• A solid foundation in generative AI, machine learning, and related areas.
• Interest in Speech/Language Processing and its application.
• Know-how/interest in designing and conducting user studies.
• Excellent communication skills and a collaborative spirit to work with multidisciplinary professionals.
• Proficiency in German and English, both written and spoken.

Why Join Us?

• Conduct pioneering research in Human-AI Interaction and Computational Interaction.
• Work in a dynamic, collaborative environment with leading experts.
• Access state-of-the-art facilities and a stimulating research setting.
• Participate in teaching and supervising activities, enriching your academic and professional skills.

Application Process

Interested candidates should submit the following documents (preferably in a single PDF) to Prof. Dr. Enkelejda Kasneci by 10 October:

• Curriculum Vitae
• Motivation letter (max. 1 page)
• Transcript of records
• English proficiency test results (if applicable)
• Contact information for 2–3 references