Improving magnetic resonance imaging with mathematics

The special research area “Mathematics of Reconstruction in Dynamical and Active Models”, funded by the Austrian Science Fund FWF, was launched in March 2025. Researchers from the University of Klagenfurt, led by Barbara Kaltenbacher (Department of Mathematics), will be contributing their expertise on inverse problems. The aim is to develop new mathematical tools for active, dynamic and model-based imaging modalities.

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AI-controlled drone swarms to inspect wind turbines in the future

Traditionally, wind turbines have to be shut down before they can be inspected for damage. This means that these wind turbines do not generate energy during the shutdown period. Furthermore, inspection costs for wind turbines tend to be high. The DORBINE project, funded by the FFG, involves a research team from the University of Klagenfurt working with industrial partner AIR6 SYSTEMS to develop a new technology that uses artificial intelligence to control swarms of drones that inspect wind turbines while they are in operation.

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Integrating the circular economy into existing economic models

The circular economy concept aims to use resources sustainably as a way to reduce harm to the environment. Although the notion of the circular economy is well established and frequently invoked in political discourse, its true economic significance and precise definition remain unclear above and beyond the familiar, often superficial statements. Paul Schweinzer (University of Klagenfurt) and Zaifu Yang (University of York) intend to change this: In the FWF-funded project “Circular Competitive Equilibrium”, they are seeking ways to combine the idea of a circular economy with the well-developed economic concepts of decentralized competition and private property.

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Using innovative methods to improve our understanding of the interplay between monetary policy and the economy

How can we improve the prediction of systemic risks in financial and economic crises? A new research project is developing innovative Bayesian methods to model dynamic covariances – with the aim of improving forecasts and supporting political decisions.

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