2 University Assistant Prae-Doc (all genders)

At the Institute of Logic and Computation, in the Research Unit of Databases and Artificial Intelligence (192-02) TU Wien is offering 2 positions as a University Assistant Prae-Doc (all genders) each position limited to expected 4 years for 30 hours/week. Expected start: October 2025.

We invite outstanding candidates to apply for a PhD position in Geometric Deep Learning. This unique appointment at TU Wien is in collaboration with AITHYRA offers an exceptional opportunity to engage in both foundational machine learning research and high-impact interdisciplinary applications in the natural sciences. The position offers access to top-tier academic and industry research ecosystems and is ideally suited for researchers seeking to push the boundaries of geometric and graph-based learning in real-world scientific contexts. The research program is flexible and interdisciplinary. Candidates will have the opportunity to pursue one or more of the following core themes from the following non-exhaustive list:

 1. Large Pre-Trained Models for Multi-Modal Reasoning

 This project explores the integration of graph-based foundation models (e.g., knowledge graphs) with large language models (LLMs) to build AI systems capable of reasoning across diverse scientific data modalities.

 2. Deep Surrogate Models for Complex Scientific Systems

 This theme focuses on building deep learning-based surrogate models that approximate complex physical and biological systems traditionally modeled by PDEs or other computationally expensive simulations. By incorporating physical priors such as conservation laws and symmetries into architectures like neural operators, physics-informed neural networks, and graph-based solvers, the project aims to accelerate simulations in life sciences.

3. Relational Deep Learning for Databases

This project aims to develop foundation models for relational databases. The goal is to build models capable of learning from richly structured or semi-structured data where traditional graph neural networks may fall short, enabling better representation, inference, and discovery in relational databases.

 4. Theoretical Foundations of Geometric Deep Learning

This theme addresses foundational questions in geometric deep learning, including the expressiveness of graph neural networks, their optimization dynamics, and their generalization behaviour—particularly in low-data or out-of-distribution settings. The work combines formal theoretical analysis with practically motivated case studies, offering a strong foundation for researchers interested in advancing the mathematical understanding of geometric deep learning.

Tasks:

• Collaboration on research and teaching tasks as well as examinations
• Cooperation and guidance of students
• Research and project activity
• Writing a dissertation and publications
• Participation in scientific events
• Assistance/Collaboration in organizational and administrative tasks

Your profile:

• Completion of a master or diploma curriculum in Computer Science, Machine Learning, Mathematics, Computational Biology, or a related field
• Experience with modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow)
• Background in at least one of the following: Graph Learning, Scientific Computing, Surrogate Modeling, or ML theory
• Interest in interdisciplinary research and real-world scientific problems (e.g., Biology, Medicine, Chemistry, Physics)
• Very good skills in English communication and writing
• Knowledge of German (Level B2) or willingness to learn it in the first year
• Very good communicative skills and team competences and innovative ability

We offer:

• A wide variety and exciting range of tasks in a collegial team
• Flexibility in working time arrangements
• A range of attractive social benefits (see Fringe-Benefits)
• Wide range of internal and external training opportunities, various career options
• Central location of workplace as well as good accessibility (U1/U4 Karlsplatz)

Entry level salary is determined by the pay grade B1 of the Austrian collective agreement for university staff. This is a minimum of currently EUR 2,786.10/month gross, 14 times/year for 30 hours/week. Relevant working experiences may increase the monthly income.

We look forward to receiving your application until September 04th, 2025.