Recruitment and Projects

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, the candidate will track 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. EU-funded, MSCA DN budget and salary.

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

Schmidt-Arras, Salzburg, Austria. EU-funded, MSCA DN budget and salary.

Doctoral candidate 3 will investigate how metabolic and cellular interactions shape metastatic niches both in vitro and in vivo. Using colon organoids and orthotopic transplantation models, combined with in vivo imaging, spatial transcriptomics, metabolomics, and immunofluorescence, the candidate will map molecular changes during metastasis. Computational modelling in collaboration with P14 will integrate these data to uncover key inter- and intracellular signalling mechanisms, aiming to predict and understand the drivers of niche formation. 

Krenn, Salzburg, Austria. EU-funded, MSCA DN budget and salary.

Doctoral candidate 4 will explore metabolic changes that support cancer progression and metastasis. Using mass spectrometry imaging, LC-MS/MS, and spatial metabolomics, the candidate 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. EU-funded, MSCA DN budget and salary.

Doctoral candidate 5 will investigate how extracellular matrix (ECM) patterning influences cell states and plasticity in colorectal micrometastases. The candidate 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. SERI-funded, MSCA DN budget and salary.

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, the candidate will uncover how brain-resident cells influence tumour growth, therapy resistance, and to identify compounds targeting both tumour and microenvironmental cells.

Tchoghandjian, Marseille, France. EU-funded, MSCA DN budget and salary.

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, the candidate 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. EU-funded, MSCA DN budget and salary.

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, the candidate will model therapy responses and identify immune mechanisms that drive tolerance or rejection in metastatic niches.

Schürch, Tübingen, Germany. EU-funded, MSCA DN budget and salary.

Doctoral candidate 9 will develop computational methods to analyze dynamic live-cell imaging data from tumor–immune–stroma interactions in primary and metastatic cancer models. Using machine learning and computer vision, we will quantify cellular behaviors, integrate imaging with spatial metabolomics, and link these features to clinical data. The goal is to build scalable analytical pipelines for interpreting complex imaging datasets in metastasis research.

Alieva and Olmeda, Madrid, Spain. EU-funded, MSCA DN budget and salary.

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, the candidate 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. EU-funded, MSCA DN budget and salary.

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, the candidate 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. SERI-funded, MSCA DN budget and salary.

Doctoral candidate 12 will establish computational methods for data dicovery. The candidate will be involved in adpating and using a scalable computational platform for integrating and managing multi-omics and imaging data across spaXio. All computational work will be based on using and developing nf-core (https://nf-co.re) analysis pipelines. The computational pipelines will efficiently prepare and integrate heterogenous data for machine learning application and thereby enable collaborative research on spatial transcriptomics and proteomics data. We will have a strong methodological focus on large-scale, reproducible cancer research using best practices in computational research.

Nahnsen, Tübingen, Germany. EU-funded, MSCA DN budget and salary.

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. EU-funded, MSCA DN budget and salary.

Doctoral candidate 14 will computationally predict signaling pathways within cells (such as kinases and transcription factors) and between cells (receptor-ligand interactions) by combining prior biological knowledge with spatial and single-cell multi-omics datasets. Using interpretable AI and network biology, the candidate will thus integrate data from the literature and from spaXio collaboration partners to mechanistically dissect molecular processes that occur in metastatic niches. 

Fortelny, Salzburg, Austria. State of Salzburg-funded, NON-MSCA DN budget and salary. EU-mobility rules do not apply.