荷兰莱顿大学医学中心肿瘤-微生物组研究博士后

荷兰莱顿大学医学中心肿瘤-微生物组研究博士后

莱顿大学（Leiden University），是一所研究型大学。成立于公元1575年，是荷兰第一所国立大学。自2018年以来，莱顿大学与中国内陆的顶尖高校建立了深度合作关系，包括清华大学和北京大学。

Postdoctoral Researcher in Tumor-Microbiome Research

Leiden University Medical Center

Leiden, Netherlands

Are you looking for a challenging research project within a leading academic medical center? And are you excited about contributing to an improved understanding of how microbiota impact human health and disease? Then we would like to meet you! While many human disorders, including tumor diseases, have been associated with changes in the gut microbiome, it is less clear which microbes can invade tumor tissues and which tumor subtypes exhibit characteristic tissue microbial signatures. Moreover, while there is early evidence for microbes colonizing certain tumors, many questions remain about how microbes colocalize and interact with specific host cell populations at the molecular level.

About your role

In this project, you will spatially map microbes in tumor tissues and analyze molecular microbe-host crosstalk by generating and integrating spatial and bulk omics data across various modalities. You will work together with other members of our interdisciplinary team as well as with external collaborators from the LUMC and beyond that share our interest in spatial omics (Leiden, NL: Noel de Miranda; Heidelberg, DE: Jan Korbel, Mathias Heikenwaelder, Julio Saez Rodriguez, Jens Puschhof, Eran Elinav; Stockholm, SE: Stefania Giacomello, Eduardo Villablanca).

About you

You hold a PhD degree in any biomedical discipline.

Excitement for high-throughput approaches and quantitative analysis.

A keen interest in embarking on ambitious research projects to gain a better understanding of the role of microbes in tumor development and patient outcome.

Demonstrated experience in one of the following areas:

Fluorescence microscopy/spatial omics technologies: Experience in fluorescence microscopy, knowledge of modern spatial technologies (including various multiplex FISH approaches), and practical experience in microbiology and/or cell culture. Ideally, experience with automated image acquisition, designing/using microfluidics devices, and/or other lab automation and high-throughput methods. Interest in applying these techniques to characterize bacterial virulence, adhesion, and invasion mechanisms relevant to cancer.

Bioimage and spatial data analysis: Theoretical knowledge and practical computational skills (preferably using Python) in quantitative bioimage or spatial omics analysis, including image segmentation, object/spot recognition, and designing/decoding fluorescence barcodes. Looking forward to utilizing these skills for the integrative analysis of new data modalities capturing microbial and host features, aiming to gain insights into molecular host-microbe interactions.

Biostatistics and machine learning applications in microbiome research: Thorough understanding and practical experience in analyzing sequencing or other omics data using statistical and machine learning methods (ideally including metagenomics or other microbiome data). Eagerness to apply such approaches in challenging microbiome projects to understand how microbes interact with the host and how microbial influences affect tumor progression and treatment outcomes.

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