The Doctoral College on Computational Sustainability attempts to achieve its vision via the following Missions:
- to establish and foster research excellence that develops cutting-edge computational models, algorithms and behavioral policies that tackle sustainability challenges in climate, energy, circular economy, mobility, biodiversity, etc.
- to establish an internationally recognized transdisciplinary research hub that advances computational methods to address sustainability challenges, fostering innovation, impact, and collaboration and collaboration across disciplines and sectors …
- to stimulate and enable individual scientific careers of PhD candidates through cross-disciplinary collaboration bridging the gap between computer, complexity and data science, engineering, environmental science and social sciences.
- to empower PhD students with the knowledge and skills to become internationally renowned (i.e. via publications, citations and other dissemination) in computational sustainability, emphasizing transdisciplinary approaches and teamwork, ethics, and innovation.
- to engage as individual researchers as well as a research hub team with industry, policy makers, and civil society to ensure research translates into tangible sustainability solutions, thus creating real-world impact.
- to collectively advance the frontier of computational sustainability through the expected 20 doctoral theses as a whole, generating a significant body of interdisciplinary knowledge that drives systemic change, informs policy, and contributes to scalable and transformative sustainability solutions.
Rationale
The rationale behind the Computational Sustainability Doctoral College is summarized along the following considerations
- JKU, therein specifically the Faculty of Technical and Natural Sciences (TNF), and TU Wien have distinct but complementary strengths in AI, machine learning, data science, engineering, sustainability sciences, mobility, energy, and architecture. Combining these allows for holistic solutions to pressing sustainability challenges, exploiting interdisciplinary synergies.
- Sustainability problems require complex, data-driven, and computational approaches. A transdisciplinary Doctoral College fosters cross-domain knowledge exchange to create scalable and impactful solutions, potentially suitable to address global sustainability challenges.
- AI, optimization, analytics and simulation are key enablers for sustainable systems in energy, climate, mobility, circular economy, and biodiversity conservation. The program shall push the boundaries of these methods for real-world applications, thus advancing computational methods for sustainability issues.
- Sustainability problems are inherently multi-faceted, requiring collaboration between computer scientists, engineers, environmental scientists, and social scientists. Here, the Doctoral College will create a research environment that encourages transdisciplinary integration, unifying disciplinary research cultures.
- The addressed new generation of PhD graduates needs not only technical excellence but also the ability to communicate, collaborate, and implement sustainable solutions. The Doctoral College will offer training in transdisciplinary methods, ethics, responsible AI, and real-world sustainability challenges, thus setting a reference for the education of next-generation researchers.
- Foster a vibrant ecosystem that brings together computational scientists, material, chemical and biological scientists, environmental experts, social scientists, legal experts, industry partners, and policymakers. Through continuous dialogue andcollaborative research, we aim to co-create transformative solutions that are both scientifically robust and contextually relevant.
- Given the strong industrial and policy networks of both universities, the Doctoral College shall align research withindustry needs, governmental policy-making, and societal impact. It will foster entrepreneurial and applied research mindsets to translate computational sustainability innovations into practice, thus bridging academic research and industry/policy impacts.
- Showcase how to develop sustainability solutions that embed ethical, social, and environmental criteria directly into computational frameworks like machine learning and AI systems. This means designing models that not only optimize efficiency but also account for equity, transparency, and long-term ecological impacts.
- Pioneer new paradigms in artificial cognition where machines “think about” sustainability by modeling and simulating complex interactions between human, environmental, and technological systems. These systems should autonomously evaluate their own sustainability impacts and adapt to evolving global challenges.
- Promote a paradigm shift toward resource-aware AI and ML systems that prioritize sustainability impact over mere computational efficiency. This includes developing models that intelligently balance energy consumption with long-term sustainability benefits, ensuring that every computational process contributes to net-positive environmental and societal outcomes.
- Advance the concept of self-optimizing, sustainability-conscious AI engines that dynamically adjust their resource usage based on real-time environmental and economic constraints. By embedding sustainability as a core optimization criterion, these systems will pave the way for AI-driven solutions that inherently minimize waste and maximize regenerative impact.