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Postdoc in multi-modal cancer data analysis at University of Zürich and ETH Zürich

University of Zurich and ETH Zurich
Zurich, Canton of Zürich (CH)
Approximately 100k CHF / year
Closing date
20 Apr 2024

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Job Type
Employment - Hours
Full time
Fixed term

The position:

Cancer is a complex tissue disease that involves multiple cell types including tumor, immune and stromal cells. These cells influence each other to dictate the outcome of the disease. Characterizing the cellular components and understanding the interactions of the tumor ecosystem is critical to identify the most appropriate treatment for each individual patient.

Our group is among the pioneers in the development and application of single-cell approaches for the comprehensive analysis of tumor ecosystems. Our mass cytometry based approaches enable simultaneous analysis of over 40 protein and transcripts in single cells in suspension and in tissues. Over the past years we have collected comprehensive imaging mass cytometry (IMC) data from over a thousand patients within the IMMUcan consortium. We also have access to RNAseq, whole-exome Seq (WES), H&E and whole slide multiplexed immunofluorescence of the same patients as well as associated clinical data.

We are looking for a highly motivated postdoc with a record of accomplishment in single-cell, spatial or image data analysis and experience with multi-modal data integration. Experience with deep-learning based data analysis will be considered a plus. The data is available and more data is being collected. This work will be embedded in a project driven by the European IMMUcan consortium of basic researchers, clinicians and data analysists from industry and academia with the goal to characterize the tumor microenvironment of up to 3000 patients by 2026. You will be based in the beautiful city of Zurich, Switzerland.


In this position, you will:

• Work in a collaborative and interdisciplinary team at the interface between the clinic, academia and industry

• Analyze and integrate highly multiplexed tissue imaging data with RNAseq, WES, and whole-slide imaging data

• Develop and apply approaches to integrate multidimensional data for the identification of biomarkers for patient stratification and treatment prediction

• Establish a deep-learning framework for IMC data and potentially for the integration with the other data types.


Ideally, you have:

• Obtained your PhD in the field of data science, computational biology, mathematics or statistics

• Previous experience with biological data analysis is required

• Single-cell, spatial data and/or images analysis experience are considered a plus

• Proficiency in R and python are required

• Previous knowledge in R package development is considered a plus

• A desire to work in a collaborative environment

• A good proficiency in English (German is optional)


To apply:

Send by email a cover letter (including an explanation of your motivation), your CV, and the contact information of at least two references to Prof. B. Bodenmiller ( This position is available immediately and applications will be considered until the position is filled.


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