1. Research Background:

• Statistical data analysis and machine learning
• Spatial transcriptomics, spatial metabolomics and image analysis
• RNA biology and gene regulation 

We are a computational biology research group at the interface of data science and biomedicine. We develop statistical and machine learning methods for the analysis of spatial omics, imaging and sequencing data. In close collaboration with experimental groups at CECAD, we study how cells and tissues are organised in space and how these processes change during ageing, metabolism and disease. 

2. Research questions addresses by the group:

How do cells coordinate gene expression and metabolism within tissues? How does ageing affect tissue organisation and cellular communication? How are these processes regulated at the level of RNA metabolism?

To answer these questions, we combine spatial omics technologies with AI, machine learning and statistical modelling. Students work with unique multimodal datasets and contribute to both computational method development and biological discovery.

3. Possible projects:

1. Spatial multi-omics of ageing tissues
   We analyse spatial transcriptomics, spatial metabolomics and microscopy data from ageing tissues. Possible topics include multimodal image registration, cell-level data integration, spatial pattern discovery and the reconstruction of metabolic and transcriptional gradients in organs such as the liver (see Nikolopulo et al.).

2. Computational methods for spatial omics
   Spatial omics technologies generate massive datasets at near-cellular resolution. Projects focus on machine learning, probabilistic modelling and scalable algorithms for spatial data integration and analysis (see Köhler et al.).

3. RNA metabolism and post-transcriptional regulation
   We investigate nuclear export, RNA quality control and RNA degradation using experimental and computational approaches. Projects may involve statistical modelling of RNA dynamics or the identification of molecular determinants of RNA processing (see Schwalb et al. and Müller et al.).

4. Applied Methods and model organisms:

Our work combines machine learning, artificial intelligence, statistical modelling and image analysis. Current topics include multimodal data integration, spatial data analysis, representation learning and probabilistic modelling.

We primarily study ageing-related processes in mouse tissues, complemented by projects involving human and zebrafish datasets.

5. Desirable skills and qualifications:

• Background in Statistics, Mathematics, Physics, Computer Science or Computational Biology 
• Experience with R, Python or related programming languages 
• Awareness of reproducible and rigorous data analysis 
• Familiarity with modern AI systems and AI-assisted research workflows 
• Interest in interdisciplinary research and quantitative modelling 

6. Selected Publications:

Köhler et al. "A spectral dimension reduction technique that improves pattern detection in multivariate spatial data." Bioinformatics (2026) https://academic.oup.com/bioinformatics/article/42/2/btag052/8450336

Nikopoulou et al. "Spatial and single-cell profiling of the metabolome, transcriptome and epigenome of the aging mouse liver." Nature aging 3.11 (2023): 1430-1445.  https://www.nature.com/articles/s43587-023-00513-y

Hussainy et al. Pseudotime analysis reveals novel regulatory factors for multigenic onset and monogenic transition of odorant receptor expression. M Hussainy, SI Korsching, A Tresch. Scientific Reports (2022), https://doi.org/10.1038/s41598-022-20106-w

Schwalb et al. "TT-seq maps the human transient transcriptome." Science 352.6290 (2016): 1225-1228. https://doi.org/10.1126/science.aad9841

Müller et al. "Nuclear export is a limiting factor in eukaryotic mRNA metabolism." PLOS Computational Biology 20.5 (2024): e1012059. journals.plos.org/ploscompbiol/article