Projects

The doctoral network Artificial Intelligence in Parkinson’s Disease (AIPD) is researching new AI-based methods for the early diagnosis and treatment of Parkinson’s disease. The goal is to develop digital approaches that enable more precise detection of Parkinson's disease – for example, by identifying speech biomarkers – and thereby support more personalized therapies. The project also addresses issues of trustworthiness, ethics, and the regulation of AI solutions.

The integration of recycled materials continues to pose challenges for the manufacturing industry, as the quality of the products depends on the interaction between the materials used and the manufacturing processes. Variations in trace elements and chemical properties affect additive manufacturing processes such as 3D printing. The GEAR-UP project aims to develop digital tools to facilitate the use of recycled materials in metal and plastics processing. Simulation-based approaches and AI methods will be used to establish resource-efficient manufacturing processes. The digital product passport developed in the project ensures the traceability of materials.

The SYNTHIA project team develops new techniques for the responsible generation and use of synthetic patient data. These data, generated using generative methods of artificial intelligence, can help overcome data protection hurdles, improve prediction models for personalized medicine, and emulate control groups in clinical studies. The validated synthetic data will then be made available on a modern IT platform, together with their possible applications.

Today, most crash tests to evaluate vehicle safety are conducted virtually. Documenting the changes to the simulated vehicle models is particularly time-consuming and costly. The SAFECAR-ML project aims to simplify this process. By combining novel methods of artificial intelligence (AI) with technical knowledge from vehicle development, the project partners from research and automotive industry want to standardize the information processing for the documentation of virtual crash tests. New is the combination of semantically processed free text with multimodal engineering data for machine learning.

Technologies such as CCUS (Carbon Capture, Utilisation, and Storage), which capture and store CO₂, are a key component of decarbonization strategies. The project "Eastern Lights" supports the development of a large-scale CCUS cluster in northern Bulgaria to store CO₂ from the cement industry. The construction of a short pipeline is planned to safely transport CO₂ to saline aquifers for storage. The goal is to establish an efficient transport and storage infrastructure that will serve as a long-term model for CO₂ utilization in the region. Fraunhofer SCAI’s software MYNTS supports the planning and optimization of the CO₂ pipeline network.

The BASE project aims to develop the digital battery passport, which every larger battery will be required to have in the future. The passport will contain continuously collected data on the "State of Health" as well as information on the supply chain, the manufacturing process and material data. It will apply the "mass balance" method, which offers a detailed accounting of the materials used in battery production, with special emphasis on the use of sustainable components. The data from the battery passport is stored in a decentralized and tamper-proof manner so that all parties involved have access to it. This enables the optimization of battery lifespan and enhances recycling.

The main objective of the ETHCSTWIN initiative is to establish a collaborative network between the Institute of Ethnopharmacological Studies and Phytotherapy (IESP, Athens) and two renowned academic research teams from Italy (UNISG, Pollenzo) and Germany (Fraunhofer SCAI), as well as the biotech SME Pangea Botanica and the University of Prishtina. The goal is to integrate the knowledge of ethnopharmacology into novel computational and digital systems, focusing on the development of a rich portfolio of complex methods and tools, including the analysis of large data sets and the restoration of tangible and intangible heritage.

The SmartEM project develops a standardized system that allows to combine different computational engineering models from different sources and to merge them into a complete system. This flexible reference architecture, inspired by open data space concepts such as Gaia-X, promotes collaboration between different actors and enables the reuse of engineering models. This makes development processes more efficient and replaces manual, time-consuming procedures for creating digital twins. The AI-based generation of "surrogate models" - simplified versions of complex models from heterogeneous data sources - facilitates model integration and thus improves interoperability. In this way, SmartEM accelerates digital transformation and innovation in engineering.

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.

The ALABAMA project is developing adaptive laser techniques for improved additive manufacturing. The goal is to decrease material porosity and adjust microstructures. The innovation includes the development of models for process optimization. Process parameters are optimized using multi-beam control and laser shaping. Advanced monitoring uses multispectral imaging for quality control. Material tests ensure compliance with requirements. The technology is being tested in aerospace, maritime, and automotive industries. These sectors face challenges regarding material quality. The project promises significant productivity increases and cost reductions. It also promotes the autonomy of the European industry.