Projects

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.

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.

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.

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.

This project aims to create a multi-scale patient-specific human virtual twin for patients eligible for CAR T cell therapy, which can be extended to cellular immunotherapies in general.

Over the next four years, PREDICTOM will develop a platform for assessing dementia risks. The platform will enable early detection, allowing timely intervention and preventive treatment.

The overall aim of COMMITMENT is to develop a computational systems medicine framework that allows clinically meaningful stratification of psychotic disorders and the identification of biological processes shared with somatic and neurodegenerative comorbidities.

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.