Partner

UFZ

Environmental health, biodiversity, pollution, climate impacts; mechanistic and operational models for water, energy, and material flows.

UFZ covers a broad spectrum of environmental research, including environmental health, biodiversity, pollution, and climate-change impacts on ecosystems and society. Researchers develop mechanistic and operational models for water, energy, and material flows as well as biodiversity and ecosystem functions, supported by strong monitoring and observation. UFZ applies modern data science—from spatio-temporal ML and explainable AI to NLP/LLMs and deep learning (e.g., graph neural networks) in toxicology—with a clear focus on interpretability and uncertainty handling.

HZDR

Interdisciplinary research across health and environment supported by strong computational/data science capacity.

HZDR brings together interdisciplinary teams spanning systems biology, earth systems science, materials research, and medicine to tackle challenges in energy, health, and matter, with strong relevance to health, climate, and sustainability (e.g., cancer, water treatment, circular economy). Data science is deeply embedded through computational groups that support experiments and scientific research. HZDR also develops intelligent miniaturized biosensors and works toward combining medical imaging modalities into real-time AI-assisted cancer treatment workflows.

CASUS

Bridging systems understanding, scientific computing, and interdisciplinary data science across domains.

CASUS integrates mathematics, systems theory, data science, scientific computing, and HPC to advance data-intensive systems research. Within IDEAS, CASUS acts as a bridge between HZDR and UFZ, bringing health- and environment-focused research under one roof via shared digital methods such as physics-informed ML and large-scale simulation. Key foci include AI-based biomedical image analysis, AI tools for cancer diagnosis and treatment, and HPC-enabled analysis of global biodiversity patterns.

Leipzig University

Link between domain sciences and data science expertise; training and supervision.

Leipzig University is a major, research-intensive university, with a strong base in data science through its Faculty of Mathematics and Computer Science and an explicit emphasis on connecting computer science to other disciplines. Interdisciplinary research groups include areas such as automatic language processing, image and signal processing, bioinformatics, and medical informatics. In Earth system research, Leipzig University links high-resolution Earth observation and remote sensing with ML, causal inference, and statistical modeling—also reflected in its international MSc in Earth System Data Science and Remote Sensing.

TU Dresden

Strong computer science and digital sciences ecosystem supporting interdisciplinary data science.

TU Dresden is a University of Excellence with a strong digital sciences ecosystem, including the Center for Interdisciplinary Digital Sciences (CIDS) and access to high-performance computing resources. Its Faculty of Computer Science is among Germany’s leading departments and offers international MSc programs with tracks spanning computational life and computational environmental sciences (including new tracks planned from 2025). The Faculty of Environmental Sciences contributes deep domain expertise in monitoring and modeling the Earth system and coordinates the NFDI4Earth consortium, creating a strong link to research data management and data science methods.

ScaDS.AI

AI center spanning methods, applied AI, responsible AI, and scalable architectures.

ScaDS.AI is one of Germany’s five national AI centers, jointly hosted by Leipzig University and TU Dresden. It spans topics from AI methods and algorithms to applied AI & big data, responsible AI, and scalable architectures, offering a strong environment for method development and translation into domain applications. ScaDS.AI also trains doctoral researchers at the intersection of data and domain science and connects to additional regional AI initiatives (e.g., SECAI, Come2Data).