肿瘤演化中先天-适应性免疫相互作用方向博士职位
Fully-funded PhD studentship: Innate-adaptive immune interactions in cancer evolution
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- 研究内容
- 项目旨在研究肿瘤演化过程中的先天-适应性免疫相互作用,重点关注组织驻留先天淋巴细胞(ILC2)及其他免疫细胞在胰腺癌、乳腺癌、卵巢癌和肺癌中的作用机制,运用高参数流式细胞术、单细胞及空间转录组学等前沿方法揭示早期癌症免疫调控机制并寻找治疗靶点。
- 申请条件
- 申请者需具备细胞生物学、免疫学、分子生物学或计算生物学等相关背景,并拥有实验室经验(如流式细胞术、细胞培养、分子生物学操作或转录组数据分析等)。
- 待遇
- 提供全额资助(Fully-funded studentship)。
- 申请方式
- 通过剑桥大学申请者门户网站(University Applicant Portal)在线申请,课程开始日期选择2027年10月,申请截止日期为2026年10月16日,申请时需注明参考编号SW51021。
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结构化信息
- 截止
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- 学科
- 免疫与微生物学
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- 导师
- Dr Tim Halim
- 来源
- University of Cambridge 官方招聘 · 最近核对 2026-10-09
- 详情核验
判定依据(原文摘录)
- is_phd
Fully-funded PhD studentship: Innate-adaptive immune interactions in cancer evolution
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Please apply via the University Applicant Portal at: https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcrpdmsc
原文
Supervisor: Dr Tim Halim
Department/location: Cancer Research UK Cambridge Institute
Deadline for application: 16th October 2026
Course start date: 1st October 2027
Project details
For further information about the research group, please visit our website at https://www.cruk.cam.ac.uk/research-groups/halim-group/
The immune system is capable of detecting cancer, which forms the basis of immunotherapy. While immunotherapy has revolutionised cancer treatment in recent years, it remains unclear how exactly the immue system detects and responds to pre-cancereous lesions. Nevertheless, it is known that the activatin (or deactivation) of anti-cancer immune cells involves many different cell-types that communicate dynamically in specific tissue niches.
Our laboratory has an interest in resolving the interactions between tissue-resident immune cells, with the overall goal of identifying key mechanisms that can be targeted therapeutically at different stages of cancer. We found that recently discovered, tissue-resident, group 2 innate lymphoid cells (ILC2) can play key roles in coordinating different types of immune and non-immune cells that play critical roles in tumourigenesis (i.e. Tregs, Dendritic Cells, cancer associated fibroblasts, etc.). The project will start by asking if ILC2, or other tissue-resident innate immune cells, play an important role during tumour evolution.
The PhD project will build on ongoing work in different cancer types (pancreatic, breast, ovarian and lung), where we have already developed mouse models and human tissue processing pipelines. The candidate will learn and use cutting edge immunological methods (high parameter flow cytometry, multiplex imaging, single cell and spatial transcriptomics) in conjuction with intravital imaging and proximity labelling tools to define the dynamic interaction of immune cells in cancer. The candidate will need to master both complex 'wet lab' techniques, as well as learn how to perform robust in silico analysis of data (training will be provided by senior lab members, collaborators, or experts in the Institute core facilities). The candidate will generate hypotheses from these large descriptive datasets, which will then be tested rigorously using in vitro assays, in vivo, and ultimately in patient derived material. The overall ambition is to uncover novel mechanistic insights about immune regulation in early cancer, and to design interventions that halt or delay tumour progression.
References/further reading
Yip T et al. Science 2026 (DOI: 10.1126/science.aea5113)
Stockis J et al. Science Immunology 2024 (DOI: 10.1126/sciimmunol.adl1903)
Schuijs MJ et al. Nature Immunology 2020 ( https://doi.org/10.1038/s41590-020-0745-y )
Preferred skills/knowledge
Cell biology / Immunology laboratory experience, such as: Flow cytometry and FACS, immunofluorescence microscopy/imaging, cell culture, tumour cell killing assays, tissue processing, etc.
Molecular biology laboratory experience, such as: DNA/RNA extraction, PCR, cloning, virus production, ELISA, etc.
Computational biology laboratory experience, such as: bulk- singl cell- or spatial-transcriptomic data analysis, spatial biology analysis using HALO, Imaris, etc.
Murine or human tissue work, such as: tissue processing, tumour or immune-related in vivo models, surgical skills, transgenic animal models, UK PIL training, etc.
Knowledge: background in cancer biology, immunology, cell biology, etc. Training and interest in bioinformatics, computational biology.
How to apply
Please apply via the University Applicant Portal at: https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcrpdmsc
You should select to commence study in October 2027.
References
We would appreciate it if you could ask your referees to submit their references as soon as possible upon request, despite the longer University deadline for references. They will receive a request once you have completed the References section of your application.
Please quote reference SW51021 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.