About the team/job
Chronic kidney failure affects about 11% of the world population, of which one-third of cases involve immune-mediated disease. Those patients who reach end-stage kidney failure require renal replacement therapy, and though transplantation considerably improves the quality of life, the longevity of transplant organs is limited, mainly due to immune rejection. Understanding which immune cells are important in the early response to antibodies, and the mechanisms by which they cause injury, underpins the development of effective future therapies. However, to do this, we need to develop better microscopy methods that are capable of imaging all immune cells within human biopsies, and powerful analysis methods to quantify these images. In this project funded by the MRC initiative for multimodal research across scales to understand human disease, we will explore the early and late stages of the immune reaction, with the ultimate goal of increasing the lifespan of transplanted kidneys.
The Uhlmann group develops methods to quantify morphology from microscopy images, whether they are 2D, 3D, static, dynamic, and of any imaging modality. Our overarching aim is to provide general quantification frameworks for bioimages to investigate living systems across scales and build bridges between mathematical modeling and image data. In this project, we will lead the image quantification and data integration efforts, teaming up with our partners from the Crick Institute (project lead), EMBL Heidelberg, EMBL Hamburg, and Imperial College London.
Your role
The successful candidate will develop the computational tools required to extract quantitative information on the visual features of kidney transplant rejection across different imaging modalities, and integrate them into a predictive model.
Several image analysis workflows will need to be designed to extract the information contained in the image data and molecular readouts acquired in the project. Among others, it will involve a pipeline to automatically segment instances of immune and endothelial cells in volumetric electron microscopy images, and a pipeline to automatically detect structural features of interest in high-resolution high throughput tomography images. Once objects of interest are identified or segmented, an important task will be to extract a representation of morphology that is sufficiently rich to infer cell or structure identity, as validated with spatial omics data. Ultimately, the aim is to quantify the wealth of visual information contained in the image data generated in this project in such a way that it becomes possible to combine it into a single multimodal latent representation of antibody-mediated kidney transplant rejection.
We expect all code produced in this project to be developed in Python, released as fully open source and made publicly available to the research community along with analysis results following reproducible research practices.
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Why join us
Do something meaningful
At EMBL-EBIyou can apply your talent and passion to accelerate science and tackle some of humankind's greatest challenges. EMBL-EBI, part of the European Molecular Biology Laboratory, is a worldwide leader in the storage, analysis and dissemination of large biological datasets. We provide the global research community with access to publicly available databases and tools which are crucial for the advancement of healthcare, food security, and biodiversity.
Join a culture of innovation
We are located on theWellcome Genome Campus, alongside other prominent research and biotech organisations, and surrounded by beautiful Cambridgeshire countryside. This is a highly collaborative and inclusive community where our employees enjoy a relaxed atmosphere. We are committed to ensuring our employees feel valued, supported and empowered to reach their professional potential.
Enjoy lots of benefits:
For more details, please see our employee benefits page.
What else you need to know
Location: | Hinxton |
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Salary: | £2,952.45 Year 1 stipend, per month |
Hours: | Full Time |
Contract Type: | Fixed-Term/Contract |
Placed On: | 2nd February 2023 |
Closes: | 15th March 2023 |
Job Ref: | EBI02077 |
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