Graphs in Artificial Intelligence
Neural Networks

Research Associate / PhD

Currently, there are no openings. If you are interested in working with us, please have a look at the speculative applications section below.

Student research assistants

There are two open positions as student research assistants at the project partner university of Kassel. For more information and on how to apply, please see the German and English job descriptionshere.

Speculative applications

All other positions have been filled, if you are interested in working with us as a PhD or postdoc, please contact , we might apply for funding together. Please include a CV, a motivation letter, two references and your transcript of records. Please also keep in mind, that it takes time to apply for funding. If you are looking for a position within a few month this option is unrealistic.

Student work

  1. Project work on the topic "Graph Type Transformations"

    • Creation of a Python package for transformations from one to another graph type (based on the paper: https://arxiv.org/pdf/2109.10708.pdf)
    • Extension of the algorithms (in algorithm type or optimized versions)

  2. For more information on this project, see theproject description.

  3. Seminar on the topic "Graph Neural Networks Decoder using Stochastic Processes (SP)"

    • Creation of an overview of similarities, differences, advantages and disadvantages of different SP which are used in Graph Neural Networks (GNNs).

  4. For more information on this seminar, see theseminar description.

  5. Seminar or project on the topic "Explainable Graph Neural Networks"

    • Create an overview of existing methods of explainability for GNN algorithms in- cluding their similarities, differences, adventages and disadventages.

  6. For more information on this seminar, see thedescription


We are funded by the Federal Ministry of Education and Research Germany (BMBF) under the following funding codes:
01IS20047A, according to the 'Policy for the funding of female junior researchers in Artificial Intelligence'.
020E-100626677, within the '7. Energieforschungsprogramm'.
16ME0877, according to the KMU-innovativ' guideline.

The responsibility for the content of this website or of any publication lies with the author.