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Christina Zielinski

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Postdoc in Computational Single-Cell Immunology and Spatial Transcriptomics University of Cambridge in United Kingdom

Degree Level

Postdoc

Field of study

Immunology

Funding

ERC-funded project ORGANise-T. Postdoctoral position; funding details beyond ERC support are not specified in the post.

Deadline

Nov 9, 2026

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Country

United Kingdom

University

University of Cambridge

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Keywords

Immunology
Computer Science
Biology
Artificial Intelligence
Computational Biology
Medical Science
T Cell Biology
Statistics
Spatial Transcriptomic

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About this position

Postdoctoral opening in computational single-cell immunology at the University of Cambridge, Department of Pathology, in Professor Christina Zielinski’s lab. The project, ORGANise-T, is ERC-funded and focuses on how human T cells adapt to life in different tissues.

The lab already has large multimodal datasets available, including scRNA-seq, TCR-seq, CITE-seq, spatial transcriptomics, and high-dimensional spectral flow cytometry. The role is ideal for candidates with a strong background in single-cell genomics and spatial transcriptomics analysis, plus experience in immunology, especially T cells.

This postdoc sits at the interface of computational biology, artificial intelligence, and immunology, in a highly collaborative and interdisciplinary environment. The successful candidate will drive the project, contribute to publications, and collaborate on side projects.

Eligibility highlights: strong relevant analytical background and domain experience in immunology/T cells.

Funding: ERC-funded postdoctoral position. The post does not specify stipend or salary details.

Application: apply by 9 Nov using the linked portal and reference PK51408. Questions can be sent to [email protected].

Funding details

ERC-funded project ORGANise-T. Postdoctoral position; funding details beyond ERC support are not specified in the post.

What's required

Strong background in single-cell genomics and spatial transcriptomics analysis, with experience in immunology, especially T cells. The ideal candidate should be comfortable working at the interface of computational biology, artificial intelligence, and immunology in a collaborative, interdisciplinary environment.

How to apply

Apply by 9 Nov using the linked application link and reference PK51408. For questions, email [email protected].

More information can be found here

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