Technische Universität Wien
Job title:
2 University Assistant Prae-Doc (all genders)
Company
Technische Universität Wien
Job description
each position 30 hours/week | limited to 4 yearsTU Wien is Austria’s largest institution of research and higher education in the fields of technology and natural sciences. With over 26,000 students and more than 4000 scientists, research, teaching, and learning dedicated to the advancement of science and technology have been conducted here for more than 200 years, guided by the motto “Technology for People”. As a driver of innovation, TU Wien fosters close collaboration with business and industry and contributes to the prosperity of society.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 ReasoningThis 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 SystemsThis 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 DatabasesThis 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 LearningThis 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
) * Wide range of internal and external training opportunities, various career options
- Central location of workplace as well as good accessibility (U1/U4 Karlsplatz)
TU Wien is committed to increasing the proportion of women in particular in leadership positions. Female applicants are explicitly encouraged to apply. Preference will be given to women when equally qualified, unless reasons specific to a male applicant tilt the balance in his favour.People with special needs are equally encouraged to apply. In case of any questions, please contact the confidant for disabled persons at the university, Mr. Gerhard Neustätter.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.
Expected salary
Location
Wien
Job date
Fri, 15 Aug 2025 05:41:27 GMT
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