Projects

The projects we work on and those we have completed are the best references for our research work. Fraunhofer SCAI is involved in numerous projects funded by the German Federal Government and the European Commission. The list below presents the projects chronologically – new projects first. You can sort the list by selecting categories.

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  • SmartEM – Open reference architecture for engineering model spaces

    ITEA-Project / Project start / April 01, 2024

    SmartEM aims to address the limitations of current engineering models by developing a reference architecture for engineering model spaces. The architecture will enable the reuse, exchange and integration of computational engineering models, reducing the need for costly design corrections and promoting early data and model exchanges. SmartEM will use AI-assisted methods to create surrogate models from heterogeneous data sources and allow their re-combination within a given engineering domain. The project will develop use-case model spaces to manage reusable and transferable engineering models for various domains and provide solutions for IP management to enable model exploitation in an increasingly digital engineering market.

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  • DeployAI

    EU-Project / Project start / January 01, 2024

    The DeployAI project aims to build and operate a European AI on Demand Platform (AIoDP). To this end, DeployAI brings together industry representatives and research institutions. The aim is to provide trustworthy, ethical, and transparent European AI solutions for use in industry – especially small and medium enterprises – and the public sector.

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  • Fraunhofer SCAI coordinates the COMMUTE project, backed by a grant from the European Commission. Over the next four years, an interdisciplinary team of top-tier experts will explore whether COVID-19 infections increase the risk of acquiring neurodegenerative diseases. An innovative AI-driven system is being developed to provide tailored risk assessments for individuals who have recovered from COVID-19.

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  • Recent years have seen an explosion in health data generation from diverse sources. This data has the potential to help advance both research and patient care. However, accessing, integrating, and analyzing it is exceptionally challenging. IDERHA aims to set up an open platform that will facilitate the integration and analysis of diverse types of health data. The platform will link up multiple public and private data sources and implement interoperable tools and services to enable key groups, such as doctors, patients, and researchers, to use the data. To focus their efforts, the IDERHA team will use lung cancer as a use case to design the platform.

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  • © EXCELLERAT

    A further increase in the performance of supercomputers is expected over the next few years. So-called exascale computers will then be able to deliver more accurate simulation results. Fraunhofer SCAI is developing efficient data analysis methods for the much larger amounts of data generated in this way, which will also provide the engineer with detailed insights into the complex technical interrelationships.

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  • Service center "WestAI"

    BMBF-Project / Project start / November 01, 2022

    The service center "WestAI" is a consortium of scientific organizations in North Rhine-Westphalia led by the University of Bonn. The services offered to customers from science and industry include support in the processing of data, in the transfer of AI technologies, in preliminary research and in the development of prototypes and customer-specific AI solutions.

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  • Patients with severe psychiatric disorders such as schizophrenia, bipolar disorder and clinical depression often develop resistance to drug therapy. Even when the early signs of treatment resistance (TR) are detected, patients must undergo lengthy procedures before medical professionals can prescribe adequate pharmaceutical care. The EU-funded Psych-STRATA project intends to analyse extensive clinical, genetic and biological data of psychiatric patients to establish criteria for early detection of TR. Based on the findings, it will propose treatment strategies for patients at risk of TR. The project will further create machine learning models that can predict TR risk and patient response to treatment, thus assisting medical professionals in providing more personalised treatment.

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