Welcome to the Young Investigator Network (YIN)
The Young Investigator Network (YIN) is the platform and democratic representation of interests for junior research group leaders and junior professors at the Karlsruhe Institut of Technology.
Visit the News Archive 2022 to learn what YIN members have recently achieved.
Complex materials based on rare earths are important for many high-tech applications such as permanent magnets or in displays. The new Collaborative Research Center "4f for Future" now investigates the synthesis and the physical properties of molecular and nano-sized rare-earth compounds targeting novel applications. As Co-PIs, Schirin Hanf and Alexander Hinz will both lead independent subprojects within this collaborative effort. KIT coordinates the interdisciplinary research center, which also involves the Philipps University of Marburg, LMU Munich and the University of Tübingen. The German Research Foundation funds it with more than ten million euros over the next four years.KIT press release
With the project ASPIRE, Julian Quinting aims at Advancing Subseasonal PredIctions at Reduced computational Effort. The idea is to make more use of sources in the atmospheric system with high intrinsic predictability such as recurring patterns of tropical convection that vary on the time scale of two weeks to two months. Patterns of convection in the Pacific, for example, have a major influence on the weather in Europe but are insufficiently represented in weather prediction models. To improve this, while keeping computing efforts at a minimum, Quinting develops machine learning models to imitate the effects of high resolution. The European Research Council will fund the project for five years.KIT press release
To bring new products faster to market, companies need to improve immature production processes on the fly. The goal of the new DFG research group at KIT is to make process adjustments cheaper, faster and more efficient with the systematic use of artificial Intelligence (AI). In the subproject "Management and Quantification of Process Maturity Improvement", Tobias Käfer uses knowledge graph-based methods to model data and knowledge around the process and make it available to experts. To address challenges like the distribution of data, its heterogeneity, and the treatment of physical knowledge, the scientists combine semantic data processing with qualitative reasoning.DFG-Forschungsgruppe