单细胞空间多组学癌症生物学博士职位

Fully-funded PhD Studentship: Single-cell Spatial Multi-omics for Cancer Biology

University of Cambridge · 英国 · Cambridge

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研究内容
该博士项目将开发和应用单细胞和空间多组学方法,以连接分子签名和癌细胞的脆弱性。
申请条件
申请者应具有生物物理、生物学、物理学、化学、生物工程、物理化学、生物医学科学或相关学科的本科或硕士学位。
待遇
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申请方式
请点击上方的 "Apply" 按钮申请,选择 2027 年 10 月开始。
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由 @cf/meta/llama-3.3-70b-instruct-fp8-fast 生成,博士岗判定置信度 90%。

结构化信息

截止
(Europe/London) 剩 8 天
学科
材料科学
合同类型
雇佣合同
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导师
Dr. Guiping Wang
来源
jobs.ac.uk(英国博士项目及学术招聘) · 最近核对 2026-10-07
判定依据(原文摘录)
  • is_phd
    Fully-funded PhD Studentship
  • english_ok
    The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
  • bachelor_ok
    Applicants should hold, or expect to gain, an undergraduate or Master's degree
原文

Supervisor: Dr. Guiping Wang

Course start date: 1 st October 2027

Project details

For further information about the research group, please visit our website at www.cruk.cam.ac.uk/research-groups/wang-group

In the Wang Group, we develop and apply interdisciplinary technologies that bridge high-resolution spatial biology and genome-wide discovery. By integrating multi-omic sequencing and quantitative imaging, we work towards a multi-scale understanding of how molecular composition and subcellular organisation shape cell function, plasticity and disease. A central biology focus of the lab is extrachromosomal DNA (ecDNA) circular DNA elements that drive oncogene amplification, intratumour heterogeneity and therapeutic resistance across many aggressive solid cancers.

This PhD project will help develop and apply cutting-edge single-cell and spatial multi-omic approaches that link molecular signatures to cancer cell vulnerabilities. Working at the interface of molecular, chemical and optical method development and cancer biology, the student will build tools that connect molecular mechanisms of gene regulation to cellular phenotypes in cancer cells, and use them to dissect how ecDNA is organised, regulated and exploited in cancer.

The balance between method development, cancer biology and computational analysis will be shaped around your strengths and interests. Applicants from a physical-sciences or computational background should not be deterred by the biology, nor biologists by the optics and coding: training will be provided in whatever you have not done before.

References/further reading

• Tang J*, Weiser NE*, Wang G* et al. Enhancing transcription replication conflict targets ecDNA-positive cancers. Nature 635, 210-218 (2024). PMID: 39506153

• Wang G, et al. Spatial organization of the transcriptome in individual neurons. bioRxiv doi: 10.1101/2020.12.07.414060

• Wang G*, Simon D*, et al. Structural plasticity of actin-spectrin membrane skeleton and functional role of actin and spectrin in axon degeneration. eLife (2019). PMID: 31042147

• Wang G, Moffitt JR, Zhuang X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci Rep 8, 4847 (2018). PMID: 29555914

• Moffitt JR*, Hao J*, Wang G* et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc Natl Acad Sci USA 113, 11046-11051 (2016). PMID: 27625426

Preferred skills/knowledge

We are looking for a curious, motivated and collaborative student with an interest in interdisciplinary science at the intersection of molecular biology, genomics, microscopy and computation. Applicants should hold, or expect to gain, an undergraduate or Master's degree in biophysics, biology, physics, chemistry, bioengineering, physical chemistry, biomedical sciences, or a related discipline.

Any of the following experience would be helpful, although we do not expect any one applicant to have all of it:

• Laboratory experience in molecular or cell biology for example mammalian cell culture, nucleic acid work, or immunofluorescence staining.

• Hands-on experience of fluorescence microscopy, or of optics and instrument building.

• Exposure to next-generation sequencing, either at the bench or in analysis.

• Programming in Python, R or MATLAB, and any experience of analysing image or sequencing data.

• A previous research project, internship or Master's thesis in a related area.

How to apply

Please apply by clicking the 'Apply' button above.

You should select start date October 2027.

References

Please 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.

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.

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