#TRANSFORMINGTOMORROW: MAKE A DIFFERENCE WITH US.

IT:U — the Interdisciplinary Transformation University in Linz, Austria offers up to two Tenure-track and Tenured Professorships on the Intersection of Economics, Finance, Law, or Policy with Computing.

Shape the future with us as a

Digital transformation is reshaping economies, financial systems, and legal frameworks, offering unprecedented opportunities and challenges. In economics, digital technologies are driving global transformations, from the green transition to structural shifts in labor markets and value chains, requiring novel approaches to resilience and human capital development. Computational Methods and complexity science provide powerful tools to understand these dynamic changes and their global interconnectedness. In finance, digital innovation is revolutionizing markets through fintech, cryptocurrencies, and ESG investing, while raising questions about data transparency and systemic risk. Similarly, in law and policy, digitization and AI are transforming legal professions and governance, necessitating new frameworks for regulation, privacy, and digital service delivery.

By bridging traditional disciplines with computer science, complexity science, and AI, we can better address these critical challenges and shape impactful solutions for the future.

WE ARE LOOKING FOR YOU  

As applicants for a Professorship, we welcome outstanding researchers with excellent research and teaching experience who have demonstrated impact in innovative areas of the relevant fields. The successful candidates will join a dynamic academic environment to lead research, foster collaboration, and contribute to teaching in fields critical to the future of economics, finance, law and policy with technological advancement.

The most critical attribute a future Professor will bring is a collaborative, interdisciplinary approach to academia, and the ambition to create real-life impact at the chosen intersection. Connecting communities and debates in computational and complexity sciences with economics, finance, law, policy and related social sciences requires interdisciplinarity. Methodologically, it requires for example, bridging the divide between complexity economics and traditional economics, combining and being knowledgeable in methods from both fields (e.g., network science and econometrics/causal analysis).

Candidates can come from a wide range of academic backgrounds that are connected to Computational Economics, Finance, Law or Policy such as for example (this is a non-exhaustive list):

Computational Economics

• Global transformations and challenges. Examples are the green transition or the future of work.
• Global value chains / supply chain networks and economies’ resilience to shocks.
• Complexity approaches to human capital, analyzing task and skill profiles of workers to understand career and schooling trajectories.
• Structural transformation of economies and diversification paths of regional and national economies.
• Globalization: how do local economies connect to each other through migration, foreign direct investment and global value chains?

Computational Finance

• Fintech and innovation in financial markets.
• Financial markets and the green transition. How are new data on carbon footprints and social impact of companies impacting on investment strategies of individual and institutional investors (ESG)? What pitfalls and opportunities arise from this? How can they help support societal goals?
• Crypto currencies. Crypto currencies have given rise to new, digital financial market institutions. The blockchain technology that supports these new currencies. contains large amounts of detailed information that sheds light on the workings of digital markets.
• Econophysics and related models to analyze catastrophic events and financial markets forecasting.
• Complexity approaches to studying financial contagion in large-scale supply-chain networks.

Computational Law & Policy

• Regulation of AI: Given rapid developments in the field of AI and its regulation around the world, how can computational methods help reinterpret the regulation of AI?
• Empirical Legal Research with computational methods: how can we interpret how and why citizens follow rules, norms and laws at scale using computational methods?
• Computational Policy Analysis: Are legal frameworks or policies achieving their desired goals?
• Digital Government: how can governments leverage new digital technologies to deliver services to their citizens?
• Regulation and data ownership: balancing new online/digital business models with privacy of individuals.
• Leveraging computational and complexity sciences to develop new policy frameworks. Multiple transformations and global challenges (digitization, migration, green transition) have led to policy responses across all levels of government, from the local to the global. Understanding these challenges requires new policy frameworks that can ingest large amounts of data but also connect to the realities that governments have to face.
• Experience in policy-related projects and an understanding of dominant debates on industrial and other types of policy making to go from policy analysis to policy implementation and ensure impact on public policy.