A cooperation of TU Darmstadt
and RWTH Aachen University

Simulation and Data Lab

The SDL Digital Patient aims to support the implementation of a digital counterpart to the patient in clinic, in order to simulate and predict the outcome of disease and therapy.

Our target audience consists of clinicians and researchers in biomedical research with suitable data but not necessarily the skillset to run simulations in HPC environments.

We offer a diverse set of methods ranging from data science and machine learning (in particular image analysis), to molecular dynamics simulations and hybrid modelling of patient-scale models.

If you have questions for other groups or general questions like access to the HPC infrastructure, have a look at our support website.

Current research topics:

  • Digital Patient ‘Oncology’ – collaboration with Prof. Tim Brümmendorf & Prof. Steffen Koschmieder (Uniklinik Aachen)
  • Digital Patient ‘Pain’ – collaboration with Prof. Angelika Lampert (Uniklinik Aachen)

Support activities:

  • Workshops with interested researchers to establish Digital Patient ‘Demonstrators’ that serve as exemplar for future implementations in other disease contexts
  • Curation of a biomedical data ‘meta’ repository that includes a list of suitable data and instructions how to access these (restricted access)
  • Consulting service to assist with HPC implementation

Project partners


Jonas Kupschus

RWTH Aachen University

Alessia Piergentilli

Uniklinik RWTH Aachen

Richard Polzin

Uniklinik RWTH Aachen

Prof. Dr. Giulia Rosetti

Forschungszentrum Jülich

Prof. Dr. Volkmar Schulz

Uniklinik RWTH Aachen

Prof. Andreas Schuppert

Uniklinik RWTH Aachen

Julian Thull

RWTH Aachen University



  • TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model (Reza Azad, Mohammad T. Al-Antary, Moein Heidari, and Dorit Merhof), IEEE Access (accepted: 25.09.2022)
  • Instance Segmentation of Dense and Overlapping Objects via Layering (Long Chen, Yuli Wu and Dorit Merhof), The British Machine Vision Conference (BMVC 2022) (accepted: 30.09.2022)
  • HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation (Moein Heidari, Amirhossein Kazerouni, Milad Soltany, Reza Azad, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, Dorit Merhof), IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2023) (accepted: 10.10.2022)
  • P. Gräbel, J. Thull, M. Crysandt, B. M. Klinkhammer, P. Boor, T. H. Brümmendorf, and D. Merhof, “Analysis of automatically generated embedding guides for cell classification,” in IEEE International Conference on Image Processing Theory and Tools and Applications (IPTA), 2022