Recruitment

Doctoral candidate 1 will investigate how tumour-derived extracellular vesicles (EVs) and metabolism prime distant organs for metastasis. Using mouse cancer models, in vivo imaging, and EV labelling, it tracks cancer spread and niche changes. Spatial transcriptomics and metabolomics are used to analyse molecular alterations. The goal is to understand how EVs enable metastasis.

Amend and Horejs-Hoeck, Salzburg, Austria. 

Doctoral candidate 2 will map how cellular interactions and metabolism in metastatic niches influence cancer spread. Using mouse models of breast and colon cancer, bioluminescent imaging, uLIPSTIC tagging, and single-cell RNA sequencing will track tumour cell interactions. Spatial transcriptomics and high-plex imaging will analyse niche changes. The aim is to uncover how immune and stromal cells support metastasis.

Schmidt-Arras and Krenn, Salzburg, Austria.

Doctoral candidate 3 will model how metabolic and cellular interactions shape metastatic niches. Using spatial transcriptomics, metabolomics, and immunofluorescence on tumour-bearing tissues, it will identify molecular shifts during metastasis. Computational models will integrate this data to reveal key inter- and intracellular signalling mechanisms. The goal is to predict and understand drivers of niche formation.

Krenn and Fortelny, Salzburg, Austria.

Doctoral candidate 4 will explore metabolic changes that support cancer progression and metastasis. Using mass spectrometry imaging, LC-MS/MS, and spatial metabolomics, it will analyse tumours, metastatic sites, and tumour-derived EVs in mouse models. Integration with transcriptomic data will reveal metabolic signatures linked to aggressive cancer behaviour. The aim is to identify targetable metabolic pathways.

Strittmatter, Munich, Germany.

Doctoral candidate 5 will investigate how extracellular matrix (ECM) patterning influences cell states and plasticity in colorectal micrometastases. It will combine tissue expansion-enhanced spatial transcriptomics with high-dimensional spatial proteomics to map ECM–cell interactions. The project aims to uncover ECM features driving metastasis progression or dormancy and identify potential biomarkers or therapeutic targets.

Watson, Lausanne, Switzerland.

Doctoral candidate 6 will develop advanced in vitro models of brain metastasis from breast cancer using patient-derived tumouroids co-cultured with microglia and astrocytes. By integrating spatial proteomics, 3D imaging, and drug screening, it aims to uncover how brain-resident cells influence tumour growth, therapy resistance, and to identify compounds targeting both tumour and microenvironmental cells.

Tchoghandjian, Marseille, France.

Doctoral candidate 7 will investigate how intra-tumour heterogeneity (ITH) shapes the immune microenvironment in primary and metastatic breast cancer. Using spatial transcriptomics and mouse models, it will map immune niches and test whether epigenetic therapies can remodel ITH to enhance anti-tumour immunity and reduce metastatic potential.

Charafe-Jauffrett and Aurand-Lions, Marseille, France.

Doctoral candidate 8 will integrate 2D high-multiplex and 3D light-sheet microscopy to characterise the tumour microenvironment of colorectal cancer metastases across organs. Using live tissue culture systems and intravital imaging, it will model therapy responses and identify immune mechanisms that drive tolerance or rejection in metastatic niches.

Schürch, Tübingen, Germany.

Doctoral candidate 9 will decode tumour-stroma-immune cell dynamics in metastatic niches using live imaging, machine learning, and multi-omics integration. By correlating cell behaviours with metabolic and clinical data, it aims to identify predictive biomarkers and therapeutic targets in brain and colorectal cancer metastases.

Alieva and Olmeda, Madrid, Spain.

Doctoral candidate 10 will develop transformer-based deep learning models to identify tumour microenvironment (TME) features associated with disease states. Using spatial proteomics data and set transformers, it will learn cell–cell interaction patterns and use attention mechanisms to reveal interpretable disease-linked TME mechanisms. The goal is to design therapeutic TME modifications in silico and identify actionable molecular targets.

Claassen, Tübingen, Germany.

Doctoral candidate 11 will integrate single-cell and spatial transcriptomic data to study tumour heterogeneity in metastatic niches. By mapping cellular phenotypes and spatial patterns, it will identify TME features associated with metastatic cell plasticity and treatment resistance. AI-driven spatial analysis will reveal prognostic markers and spatial drivers of therapeutic outcomes.

Gottardo, Lausanne, Switzerland.

Doctoral candidate 12 will create a scalable computational platform for integrating and managing multi-omics and imaging data across spaXio. It will implement FAIR data models, harmonise metadata, and establish nf-core analysis pipelines. The platform will enable collaborative analysis and machine learning applications on spatial transcriptomics and proteomics data, supporting large-scale, reproducible cancer research.

Nahnsen, Tübingen, Germany.

Doctoral candidate 13 will construct digital twins of tumour-relevant cell types to simulate interactions in metastatic niches. By integrating single-cell and spatial multi-omics data, these models will predict immune responses and therapeutic outcomes. The aim is to identify and validate intervention strategies for metastatic breast and colon cancers using digital simulations and experimental co-culture systems.

Morlot, Paris, France.

spaXio doctoral candidates will be supported by experienced core facility scientists and technicians. Associated positions will be announced soon.