C²D² Subclusters | ZHAW Life Sciences und Facility Management

C²D² is organized into subclusters that form the backbone of our activities. Each subcluster brings together expertise and resources around a specific area of computational laboratory medicine, serving as reference points for collaboration and innovative projects.

Illustration of a network of three-dimensional cubes representing digital health research areas. At the center is a large blue cube labeled “C²D² – Cluster of Competence in Digital Diagnostics.” Surrounding it are smaller blue cubes labeled “Image-based Lab Diagnostics,” “Molecular Biomarkers,” “Biomedical AI,” “Translational Medicine & Omics,” and “One Health,” embedded among many white cubes. The cubes are arranged in a connected, abstract structure, symbolizing interdisciplinary collaboration in digital diagnostics.

At the core of C²D² are five subclusters that connect different fields. Projects often span multiple subclusters, drawing on combined expertise to address complex questions.

Biomedical AI

Molecular Biomarkers

Translational Medicine & Omics

One Health

Image-based Lab Diagnostics

Head: Dr. Norman Juchler

This subcluster focuses on the development and application of computational methods for diagnostic imaging, with emphasis on:

/ Exploring novel methods in image acquisition, processing and AI-driven analysis

/ Advancing new applications for laboratory and clinical diagnostics

/ Optimizing diagnostic workflows for precision, speed, and reproducibility

Activities span areas such as digital pathology, hematology and microbiology diagnostics.

Biomedical AI

Head: Dr. Ahmad Aghaebrahimian

This subcluster advances language-based AI methods for biomedical and clinical applications:

Developing natural language processing (NLP) techniques for biomedical texts and clinical records
Applying large language models (LLMs) and other NLP techniques for information extraction and decision support
Creating tools for structured knowledge management and workflow integration
Assessing reliability, accuracy, and ethical aspects of AI in biomedical applications

Molecular Biomarkers

Head: Prof. Sandro Manni

Research in this subcluster investigates molecular biomarkers for laboratory and clinical diagnostics:

Identifying and evaluating biomarkers using clinical chemistry, proteomics and computational methods including AI
Developing biomarker-based approaches for diagnosis, prognosis, disease monitoring and prevention
Validating biomarkers across diverse laboratory and clinical settings

Translational Medicine & Omics

Head: Dr. Adisa Trnjanin

Laboratory research is linked with clinical practice through omics, next-generation sequencing and translational approaches:

Handling large-scale and heterogeneous omics datasets across different molecular levels (genomics, transcriptomics, proteomics, metabolomics)
Developing computational methods for multi-omics data integration and interpretation
Using Omics insights for disease mechanism analysis, translational diagnostics and patient stratification

One Health

Head: Dr. med. vet. Julia Traversari

At the human–animal–environment interface, this subcluster addresses key health challenges by:

Developing digital and image-based tools for pathogen detection (e.g. mycobacteria, gram-stained bacteria)
Establishing standardized digital workflows in microbiology and pathology to ensure precision and reproducibility
Exploring integration of human, animal and environmental health data to enable future monitoring of infectious diseases and antimicrobial resistance