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Apheris and T-Systems expand federated medical research on sovereign german AI infrastructure. The partners combine hospital-based model training with Industrial AI Cloud computing capacity to support protected clinical research and drug discovery across Europe. Illustration of hospitals connected through digital infrastructure, with research teams analysing medical imaging data at their respective locations. - Credit: Deutsche Telekom AG Apheris and T-Systems are enabling European customers to run federated clinical and drug-discovery research networks on sovereign cloud infrastructure. Their approach keeps patient data within the originating hospitals while privacy-compliant model parameters are combined on the Germany-operated Industrial AI Cloud, using NVIDIA GPUs. Apheris reports a clinical network involving data from more than 50,000 patients, and the collaboration is being presented at NVIDIA GTC in Berlin on 20-22 October 2026. (PRESS RELEASE) BERLIN, Germany, 21-Oct-2026 - /EuropaWire/ - Apheris and T-Systems are joining forces to enable secure use of hospital data in clinical and pharmaceutical research through federated networks hosted on sovereign European computing infrastructure. Hospitals hold valuable treatment data, but making information available for research spanning multiple institutions can require significant work. Apheris, an AI company serving biotechnology and pharmaceuticals, operates federated networks that allow participating organisations to train models and analyse protected datasets without moving the underlying information away from each partner's own environment. NVIDIA contributes through collaboration involving AI models, software and accelerated computing. Customers can now operate Apheris' federated networks for drug discovery and research using healthcare data in Europe on T Cloud Public's sovereign infrastructure, with GPU capacity supplied through T-Systems' Industrial AI Cloud (IAIC). The partners are presenting their collaboration at the NVIDIA GPU Technology Conference (GTC), held in Berlin on 20-22 October 2026. Managing the complexity of clinical data research. Multi-site studies involving treatment data must meet the General Data Protection Regulation and other data-protection requirements governing access and processing. Institutions also need to coordinate their internal policies and contracts, meaning approval processes and technical preparations can slow the launch of research projects. Federated learning provides two layers of protection. The Apheris approach brings an AI model to each hospital's own environment for local training rather than transferring patient data to a central repository. Privacy-compliant model parameters generated during local training are subsequently sent to T-Systems' Industrial AI Cloud, where Apheris combines them into a global model. The cloud is operated in Germany and uses NVIDIA GPUs, while patient data remains on site. Supporting secure use of health data. Ferri Abolhassan, CEO of T-Systems, said: "Health data is among the most sensitive data of all. Anyone conducting research with it needs reliable, secure, sovereign infrastructure," "With Industrial AI Cloud, we offer the right solution. Together with Apheris, we enable medical progress without sensitive data leaving the hospital." Robin Röhm, CEO and co-founder of Apheris, said: "Patient data must remain protected, which is why research can barely use it today. In a world where AI drives progress, it is crucial to make this data usable without giving up patient protection," "With federated learning, we bring the model to the data so it can learn from it while the data remains protected where it is. With T-Systems, this now also takes place on sovereign European infrastructure." Clinical networks already tested. According to Apheris, a healthcare data network now links several hospitals and covers information from more than 50,000 patients, including electronic health records and medical images. Researchers can analyse data directly at each participating location without aggregating patients' records centrally. A feasibility study published in 2025 examined multiple sclerosis lesion segmentation using 512 MRI cases from three clinical institutions. The images remained at the respective sites throughout the research. Applications in drug discovery. Pharmaceutical companies also employ Apheris networks to jointly train AI models without disclosing proprietary information. Nine leading drugmakers participate in the AI Structural Biology (AISB) Network to develop structural-biology AI. Other federated networks focus on small-molecule property predictions (ADMET) and the developability of antibodies. Apheris uses NVIDIA FLARE as its central federated-learning framework and contributes fixes for its production deployment. NVIDIA cuEquivariance kernels help accelerate training and inference for compatible workloads, including OpenFold3. The ApherisFold platform also integrates Nvidia MSA NIM to speed up multiple-sequence alignment generation. About Apheris. Apheris operates large federated data networks for the life sciences industry and uses them to train AI models for drug discovery and clinical research. The networks cover co-folding, binding affinity, ADMET, in vivo PK, and antibody developability, as well as Real-World Evidence (RWE) based on patient data. Within these networks, leading pharmaceutical companies and healthcare organizations train models on proprietary, distributed data that never leaves its environment, helping protect data, intellectual property, and patient privacy. The result is models that can outperform publicly available alternatives, especially where those alternatives are limited by insufficient data. They are delivered through Foundry, an AI-first platform embedded directly in partners' research workflows as modular building blocks that agents can call.

Deutsche Telekom, CURRENTA and Wandelbots test digital-twin robotics for laboratory automation. ELARA prototype uses the Industrial AI Cloud, NVIDIA Isaac Sim and Wandelbots NOVA to explore safer, more flexible workflows before any physical robot deployment Illustrative image for the ELARA project: A laboratory employee looks at a digital simulation of a robotic arm that could take over routine tasks in the lab in the future. - Credit: AI generated by Deutsche Telekom CURRENTA, Wandelbots and T-Systems are developing ELARA, a virtual laboratory automation concept intended to test robotic workflows in a digital twin before possible deployment in environmental laboratories. The project runs NVIDIA Isaac Sim and Wandelbots NOVA components using T-Systems' sovereign Industrial AI Cloud infrastructure in Germany. It targets repetitive and potentially hazardous sample-handling tasks and will be presented at NVIDIA GTC in Berlin from 20 to 22 October 2026; the partners have not announced physical robot deployment. (PRESS RELEASE) BERLIN, Germany, 20-Oct-2026 - /EuropaWire/ - Chemical park operator CURRENTA and robotics company Wandelbots are examining how digital twins and robotics could automate complex, highly variable workflows in an environmental laboratory through the ELARA project. The initiative aims to explore whether robots could eventually take on routine tasks such as pipetting and sample handling, including activities involving potentially hazardous substances. Reducing employees' direct exposure, shortening processing times and improving availability are central objectives. T-Systems supplies the sovereign infrastructure through its Industrial AI Cloud, supporting the processing of sensitive laboratory data within the European legal framework. The partners are presenting ELARA at the NVIDIA GPU Technology Conference (GTC) in Berlin from 20 to 22 October 2026. Virtual trials before deployment in the laboratory. ELARA follows a simulate-first approach: before any physical laboratory deployment, robotic tasks can be rehearsed in a digital twin to identify potential errors at an early stage. T-Systems provides sovereign infrastructure for the NVIDIA Isaac Sim simulation environment and Wandelbots NOVA components within the Industrial AI Cloud, enabling workflows to be developed and validated on NVIDIA GPU-accelerated infrastructure hosted in Germany. The project combines Wandelbots NOVA with NVIDIA Isaac Sim, built on NVIDIA Omniverse, to model and validate robotic laboratory processes in a physics-based digital twin. The NOVA extension for Isaac Sim allows robot movements to be simulated and tested before workflows are transferred to physical machines. OpenUSD underpins the 3D scene and robot models, while NVIDIA PhysX supports the physics-based simulation. The partners have already reconstructed the laboratory workflows, assessed their economic viability and defined the technical configuration. They are now exploring whether these workflows could be implemented using real robots at a later stage. Sovereign computing infrastructure and industrial data. "Europe's and its industry's greatest asset lies in their data. ELARA shows how this data can be used to trial complex laboratory workflows virtually and prepare them for robotic deployment. The Industrial AI Cloud provides the sovereign computing power needed - and lays the foundation for easing employees' workload in routine tasks and potentially hazardous procedures in the future," said Ferri Abolhassan, CEO of T-Systems. "For CURRENTA as a chemical park operator, digitalization and automation are key areas of development. They enable greater workplace safety and better availability, ultimately delivering significantly more value to our customers. Laboratory requirements are highly specific, so we are pleased to be developing the first prototype together with our partners Wandelbots and T-Systems," said Hans Gennen, COO of CURRENTA. How the ELARA concept works. ELARA stands for "Environmental Laboratory Automation and Robotics for Analytics." It is designed to represent different laboratory processes and robot types in a digital environment, including standardised stages in the handling of water and wastewater samples. Unlike highly standardised facilities that process large volumes of identical samples, CURRENTA's laboratories handle smaller quantities of diverse samples requiring different workflows. The proposed automation is intended to reduce repetitive work and free up employees for complex assessments and advisory activities. Future robots could also take on tasks involving potentially hazardous substances or contaminated wastewater samples. "Fully automated analytics already exists, for example for large volumes of identical samples in clinical testing. Our laboratory work, by contrast, involves a wide variety of samples and changing workflows. With a digital twin, we want to explore how these varied laboratory processes can also be automated flexibly," said Thomas Westfeld, project manager at CURRENTA. Wandelbots NOVA software could later enable simulated processes to be transferred to physical robots and their operation managed centrally. Over time, operational laboratory data would feed back into the simulation to support ongoing workflow improvements, with the Industrial AI Cloud supplying the necessary computing resources. Wandelbots and the Industrial AI Cloud. Wandelbots has been an anchor customer in the Industrial AI Cloud partner network since its launch in February 2026. It uses AI computing capacity to connect digital twins of production facilities and manufacturing data with robotics and to develop AI applications for manufacturing. CURRENTA broadens the project's applications. ELARA extends the Industrial AI Cloud's potential applications into laboratory automation. CURRENTA operates CHEMPARK at Leverkusen, Dormagen and Krefeld, contributing laboratory-process expertise to the project. Wandelbots provides its NOVA robotics software, and T-Systems delivers sovereign AI infrastructure and computing resources from Munich while connecting technology partners with industrial users. Together, the partners are establishing technical foundations for possible future robot-assisted laboratory operations. About Deutsche Telekom. Abir Kassis Trainee Corporate Communications EDITOR'S PICK: