The Research Alliance Ruhr is a joint undertaking of the three major universities in the Ruhr area and was initiated by the Ruhr Conference. The four research centers focus on “One Health Ruhr”, “Chemical Sciences and Sustainability”, “Trustworthy Data Science and Security,” and “Future Energy Materials and Systems.” In addition, the Research Alliance Ruhr has established a “College for Social Sciences and Humanities”.

The Research Alliance Ruhr will appoint up to 50 research professors in the next few years and also offer numerous positions for research assistants. Join us now to create innovations for the world of tomorrow in Europe's densest university landscape, where you can find a wide range of scientific and industrial partners right on the doorstep.

The Ruhr area, one of Europe's largest metropolitan regions, offers attractive career opportunities for excellent scientists and scholars from around the world. In 2021, Ruhr University Bochum, TU Dortmund University and the University of Duisburg-Essen established the Research Alliance Ruhr to bundle their cutting-edge international research on the most urgent challenges facing humankind. There are four research centers and a college. This is just the latest chapter in our long-standing collaboration as the University Alliance Ruhr (UA Ruhr), a community of 14,000 researchers and 120,000 students in the heart of Germany.

As part of the Research Alliance Ruhr, the Research Center Trustworthy Data Science and Security is seeking to fill the following position at the TU Dortmund as soon as possible:

Young Investigator Group Leader (m/f/d)
Reliable Graph Machine Learning

Payment according to public service´s agreement: TV-L E14 full-time (part-time possible).
Create an independent research group focused but not limited to:

Adversarial Attacks on Graph Neural Networks
Adversarial Attacks on Node Embeddings
Neural Network Architectures for Graphs
Generative Models for Graphs
Geometric Deep Learning
Representation Learning
Bayesian Methods for Graph Machine Learning Robustness Certificates, Reliability Guarantees or Uncertainty Quantification supporting Trustworthy Machine Learning

Excellent degree (doctorate) in machine learning, artificial intelligence or related research areas
High degree of independence to create and lead an independent research group within an interdisciplinary research environment
Experience with interdisciplinary research projects with fundamental theoretical research and practical applications

We work in a multidisciplinary team on collaborative research projects jointly envisioned by leading international experts from different domains. We aim at both theoretical research as well as practical applications in close collaboration with academic and industrial partners. The position is embedded in a creative, attractive, and internationally renowned research environment. With your research, you will play a primary role in the development of our new Research Center and outreach with trustworthy technology to the general public. Our international network of researchers and industry partners ensures a seamless transition into your next career step as university professor or international research institutions. A balanced and family-friendly work-life relationship is important to us; thus, we offer options for flexible working times or part-time remote home-office.

Career Opportunities
Our Research Center will hire 12 research professors in the next few years and offer numerous positions for research assistants and research group leaders. Join us now to create trustworthy innovations for the digital world of tomorrow in Europe's largest metropolitan region.

Diversity
The TU Dortmund University promotes diversity and equal opportunities. Convince us with your personality and expertise. Applications from women will be given preferential treatment in accordance with the legal regulations. Application of women or suitable severely disabled persons are desired.

Research Alliance Ruhr jobs

Young Investigator Group Leader (m/f/d) – Reliable Graph Machine Learning