GOETHE UNIVERSITÄT FRANKFURT - Two professorships at the interface between classical network science and graph machine learning

A large fraction of groups working at C3S and its environment will require representing, analyzing, and modeling the structure and dynamics of complex social, scientific, and technological systems as networks or graphs. Therefore, foundational research on computational network analysis will be a core topic at the center with strong links to most of its application areas. C3S is hence seeking to fill the positions (open discipline; and open rank: W1 to W2 Tenure Track; W2 to W3 Tenure Track; W3) of: Two professorships at the interface between classical network science and graph machine learning

Frist: 02.04.2024

Goethe University Frankfurt am Main is one of Germany's prominent universities, and a founding member of German U15, the association of Germany's leading research universities. Goethe University thrives in the dynamic and cosmopolitan environment of Frankfurt and the Rhine-Main region, acclaimed for its economic vitality and cultural diversity, and renowned for its eminent quality of life. This backdrop provides an ideal setting for academic pursuits, research innovation, intellectual exploration, and inspiring teaching. Covering a wide range of disciplines, Goethe University is committed to academic excellence, which is evident in our robust research programs, diverse range of disciplines, and a strong network of international collaborations.

Center for Critical Computational Studies | C3S

C3S, Goethe University’s latest initiative, epitomizes this ethos. As an innovation hub of inter- and transdisciplinary exploration, C3S is dedicated to coupling diverse academic fields, encompassing computer sciences, natural and life science, and social sciences, as well as health, economics, law, and the humanities. This unique collaboration aims to define and foster Critical Computational Studies. Its aims are threefold: To deepen our understanding of complex systems through a critical-computational lens; to scrutinize the impact of computational technologies in shaping societal realities; and to craft strategies for the responsible design and utilization of these technologies, emphasizing sustainability, trustworthiness, and justifiability.

In line with this ambitious vision, C3S invites applications for an explorative workshop (see below) from talented and visionary researchers who are keen to contribute to this venture. Ideal candidates possess a robust academic background, high-profile publications, a track record of cross-disciplinary collaborations, and a profound interest in the intersection of technological and normative approaches. Goethe University offers a stimulating academic environment, comprehensive support, and ample opportunities for research, teaching and transfer. Applicants will find in C3S a platform to not only advance their research but also to influence the future trajectory of Critical Computational Studies. They will join a vibrant community of scholars committed to making a tangible impact in both academic circles and broader society.

4 Professorships (all gender welcome), open rank and open discipline

As part of our founding strategy and in our first round of recruitment, C3S is seeking as soon as possible to fill the positions of

  • Two professorships at the interface between classical network science and graph machine learning
  • Two professorships for the modeling of climate change, namely:
    one for the modeling of the social and/or socio-economic drivers and impacts of ongoing climate change
    one for the modeling of ecosystems and/or biodiversity and their interrelation with ongoing climate change

The calls are open discipline and open rank (W1 to W2 Tenure Track; W2 to W3 Tenure Track; W3). For more information on these profiles, see below.

Explorative Workshop

In an explorative workshop from 25 to 27 June in Frankfurt am Main, we will invite candidates to showcase their expertise and crossdisciplinary interests in a dynamic, engaging, and collaborative environment. Neither your application for the workshop nor the workshop as such are part of the formal hiring process. The workshop will pave the way for this hiring process, which will be conducted by extraordinary selection committees (“Findungskommissionen” in German) pursuant to the Rules of Recruitment (“Berufungssatzung” in German) of Goethe University.

We invite applications for the workshop until 2 April 2024. You will hear back from us until 22 April 2024. We will cover travel and accommodation for invitees.

Goethe University

At Goethe University, we pride ourselves on being a family-friendly institution. We understand the challenges of balancing academic and family life, especially in the dynamic and demanding environment of academia. To support our staff and students in this regard, we offer a range of initiatives and services designed to facilitate a healthy work-life balance. These include flexible working arrangements, childcare facilities, and family support services. Additionally, we recognize the importance of supporting dual-career couples and have specific services to assist partners of newly appointed faculty in finding suitable employment opportunities in the region. Our goal is to create an inclusive and supportive environment where all members of the Goethe University community, regardless of their family situation, can thrive both professionally and personally.

Goethe University is steadfast in its commitment to fostering equal opportunities, embracing diversity, and ensuring inclusion in all its endeavors. We particularly encourage applications from qualified women and individuals with a background of migration, as we place significant emphasis on cultivating a family-friendly university work environment. Furthermore, candidates with severe disabilities, or those with equivalent status, will receive preferential consideration when qualifications are equal. This inclusive approach also extends to supporting women in fields where they are currently underrepresented, underscoring our dedication to promoting a balanced and diverse academic community.

Two professorships at the interface between classical network science and graph machine learning

Traditional measures in network science focus on the analysis and modeling of complex networks from the perspective of network structure, such as centrality measures, clustering coefficients, and motifs and graphlets, which have become basic tools for studying and understanding graphs. In comparison, deep learning models - especially graph convolutional networks (GCNs) - are particularly effective at integrating additional node features into graph structures via neighborhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. While these two classes of methods have their own strengths and weaknesses, there are great benefits to be realized from a closer integration and awareness of the two research areas and their communities.

On the one hand, GCNs gracefully incorporate various rich data features, which are largely overlooked in traditional structural measures. On the other hand, traditional network science notions, being the foundations of understanding and characterizing complex networks, are also indispensable in studying GCNs.

With complementary backgrounds in either or both of the fields discussed above, the two newly established C3S professorships will conduct edge-cutting collaborative research and teaching on the design and analysis of computational tools for identifying, explaining, and understanding the patterns in networks relevant for C3S. This may include but is not limited to graph theory, network dynamics, community detection or causal inference in networks.

Ideal candidates for these professorships will possess a robust crossdisciplinary profile or interest, showcasing not only expertise within their specific domains but also a genuine openness to collaborative, crossdisciplinary work. While the primary focus is on researchers with a strong background in network science, computer science or mathematics, proven by a matching PhD and high-ranking publications in the research areas described above, other degrees in suitable application areas are also welcome.
Envisioned research fields include but are not limited to:

  • Mathematics of graph machine learning.
  • Robustness and guaranteed performance, in particular dealing with noise, data bias and application specific challenges (e.g., in social/socio-ecological settings).
  • Sustainability, in particular scalability, data and resource efficiency.
  • Non-static graphs: dynamic, temporal, streaming settings.
  • Random graphs and random processes.
  • Dealing with higher-order structures such as motifs, graphlets, and simplicial complexes.