Suitable candidates for this project will have a degree in a relevant quantitative discipline (e.g. Computational Biology, Bioinformatics, Genetics, Biotechnology, Engineering or Mathematics). The candidate should have or should be expecting at least an upper second class honours degree, and should have strong background in the field of genomics and high-throughput data analysis. Experience with developing statistical and machine learning methods for analysis of genomics data would be a plus. The project will commence in July 2021 and has funding for 3 years. The student will be based primarily at the Barts Cancer Institute, Barts and the London School of Medicine and Dentistry (SMD), Charterhouse Square in the City of London.
Cancer cells maintain an intrinsic plasticity that allows them to reversibly change their phenotype in response to microenvironmental signals and switch between cellular states. Single-cell studies have revealed extensive transcriptional heterogeneity along with lineage mixing and plasticity in several cancer types. In metastases, cells co-opt developmental programs and are reset to an even more primitive differentiation state, mimicking organ formation to reinitiate growth in a new location.
Dr Efremova’s Lab at the Barts Cancer Institute investigates the cellular and molecular mechanisms that promote cancer cell plasticity and adaptation in the metastatic niche. Specifically, we combine computational and experimental approaches including single-cell multi-omics, spatial imaging-based data and computational methods to reconstruct the cellular environment of primary and metastatic cancers and dissect the regulatory and cell-cell communication networks that promote metastasis and therapy resistance, focusing on pancreatic and colorectal cancer.
This project will focus on integration of single-cell multi-omics and spatial imaging-based data from patient samples to interrogate the mechanisms underlying plasticity in metastasis. The position offers an opportunity to apply and develop computational methods to study plasticity and the cellular communication and gene regulatory networks that promote it. The student will be integrated in a multidisciplinary community and work closely with clinicians, biologists and computational scientists.
Academic Entry Requirements
These studentships are open to graduates with
The successful candidate should have strong background in the field of genomics and high-throughput data analysis. Experience with developing statistical and machine learning methods for analysis of genomics data would be a plus.
English Language Requirements
Applicants for whom English is not a first language will also require a minimum IELTS score of 6.5 (with 6.0 in the written component) or equivalent, unless your undergraduate degree was studied in, and awarded by, an English speaking country. For more information on acceptable English language qualifications please see here.
The funding for this studentship only covers tuition fees at the home/EU rate. Overseas applicants are welcome to apply, but will be required to fund the difference in tuition fees.
The studentship includes the following funding for 3 years:
*If you are considered an overseas student for fee purposes, you are welcome to apply for this studentship, however you will be required to cover the difference in tuition fees.
To apply you will need to complete an online application form.
The following supporting documents will be required as part of your application:
Successfully shortlisted candidates will be invited to an interview at Barts Cancer Institute.