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.

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.

The Earth's life support systems, crucial for sustaining human civilization, are currently imperiled by complex and interconnected challenges manifesting at a planetary scale. In light of these profound threats, Goethe University is dedicated to comprehending the intricacies of this multifaceted crisis, which encompasses climate change, biodiversity loss, pollution, and societal upheavals. To effectively address these issues, Goethe University pioneers multi-, inter-, and transdisciplinary research and teaching with a cross-cutting focus on ·Earth·Nature·Society·. Our key regional partners are the Senckenberg – Leibniz Institution for Biodiversity and Earth System Research, the Leibniz Institute for Financial Research SAFE, the Leibniz Peace Research Institute, the Institute for Social-Ecological Research, the Max Planck Institute for Chemistry in Mainz, and Deutscher Wetterdienst.

Within C³S, we are establishing a core research group that will be dedicated to analyzing and modeling coupled geophysical, ecological, and social/socio-economic aspects, especially drivers and impacts of contemporary climate change. This research group, spearheaded by three newly appointed professorships, explores the complex interactions and tipping points inherent in these systems.

These professorships are integral to advancing our understanding of these interrelated challenges, calling for expertise that transcends disciplinary boundaries and contributes to interface modeling. The emphasis is on improving our capabilities to simulate the dynamics of social and/or socio-economic systems, ecosystems and biodiversity, and coupled human-environment interactions at various scales, from local to global.

The selected candidates will be at the forefront of leading research endeavors in both ecosystem/biodiversity and social/socio-economic modeling. The methodological focus will be on advancing sophisticated modeling techniques, ranging from agent-based and process-based mechanistic biophysical modelling to hybrid approaches coupling also from statistical and AI methods. Of particular interest is the proficiency in navigating complex model settings, encompassing multiscale simulations, and a keen understanding of data analysis and data assimilation techniques as well as the integration of multiple data types, and parameter estimation and optimization.

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. They will demonstrate experience or a keen interest in integrating diverse aspects - ranging from physical and geological dimensions to biodiversity and ecosystem dynamics, and socio/socio-economic processes - into holistic models that address the polycrises of climate change, biodiversity loss, chemical pollution and their societal implications as a systemic planetary challenge. We envision development of crossdisciplinary models that not only forecast and comprehend complex feedback loops between environmental changes, changes in biodiversity and ecosystem services, and societal responses but also illuminate critical junctures where collective actions can bring about transformative changes.

Professorship for the modeling of the social and/or socio-economic drivers and impacts of ongoing climate change

We are specifically seeking an individual adept at deploying innovative computational methodologies. The objective is to unravel the intricate dimensions of social, climate and nature interactions, thereby contributing significantly to our understanding of the challenges at hand.

At the core of these explorations is the strategic utilization of computational technologies to simulate and model social and/or socio-economic systems and how they interface with geophysical and ecological systems in a changing climate. The integration of social and socio-economic variables into climate, Earth system and climate impact models is pivotal for a nuanced comprehension and prediction of the multifaceted impacts of climate change on societies, cultures, political, legal and economic orders etc. A better understanding of drivers is necessary to successfully mitigate climate change. Notably, the considered models should transcend current standard approaches, explicitly accounting for nonlinear aspects of societal, socio-economical, and climate dynamics.

The methodology employed should encompass the development and refinement of models that advance our understanding of the intricate relationships between climate dynamics and human systems. This includes identifying and understanding social or socio-economic tipping points within the context of global environmental shifts. Proposals are invited from researchers who specialize in developing and building data-driven social/socio-ecological models, emphasizing perspectives of system resilience, sustainability, and encompassing insights from data, economic, and social sciences.

The focus lies on social and/or socio-economic dimensions of climate change modeling. Possible focal areas include, but are not limited to:

• Socio-ecological climate change adaptation and mitigation strategies, exemplified by green infrastructure, urban planning, reforestation (nature-based solutions)  and carbon pricing.
• Eco-justice and eco-regulation, delving into uneven distributions of the causes and impacts of global warming.
• Eco-politics, exploring phenomena such as migration, war and conflict, and shifts in voting behavior induced by failures of Earth's life support systems.
• The nexus of climate change with social inequality, migration patterns, and evolving societal norms, elucidating intersections and influences.

Professorship for the modeling of ecosystems and/or biodiversity and their interrelation with ongoing climate change

We seek candidates possessing a robust background in modeling spatio-temporal dynamics of biodiversity and ecosystems under contemporary or future climate change, with a focus on how environmental shifts influence the structure and functioning of ecological communities, ecosystems and ecosystem services over time. The objective is to delve into the impacts of ongoing climate change and the changes invoked by human-nature interaction on these intricate systems. Particular attention should be paid to ecosystem and socio-ecological robustness, resilience and adaptive strategies, or their respective failures.

Research efforts should contribute to the refinement of climate adaptation and mitigation measures as well as conservation and ecosystem management strategies. This includes assessing the efficacy of habitat restoration initiatives, species reintroductions, re-vitalizing ecosystem resilience as well as various conservation and ecosystem management measures in the face of dynamic environmental challenges. The selected candidate will play an instrumental role in shaping a sustainable future by providing essential insights into the intricate dynamics of biodiversity and ecosystems in response to the multifaceted challenges posed by climate change and human-nature interaction.

Research and application areas for this professorship may include, but are not limited to:

• Exploration of how species' traits and genetic diversity evolve in response to environmental changes, elucidating subsequent impacts on ecosystem dynamics.
• Role of functional traits of species in response to climate change and impact on ecosystem processes.
• Predicting changes in species distributions due to climate change, incorporating physiological responses to temperature, humidity, and other environmental variables.
• Eco-evolutionary modeling of biodiversity dynamics and ecosystem functioning.
• Modeling interactions between climate change, biodiversity loss and environmental pollution.
• Modeling feedback from biodiversity and ecosystem change on climate.
• Modeling trade-offs and synergies between biodiversity protection, climate mitigation, sustainability goals, and ecosystem services.