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(MRC DTP) AI-scBio: Artificial Intelligence for Single Cell Biology


Faculty of Biology, Medicine and Health

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Dr C Yau , Prof M Rattray No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

The rapid development of novel microfluidic technologies and combinatorial indexing strategies, combined with decreasing sequencing costs, has empowered single-cell sequencing technology in the last five years. It is possible to routinely analyse thousands—or even millions—of cells in a single experiment transforming the data landscape in ‘omics-based biology and posing substantially novel data science problems.

Bringing together experts in the Single cell biology Research hub (https://www.singlecell.manchester.ac.uk/)
and the Institute of Data Science and AI (http://www.datascience.manchester.ac.uk/) at the University of Manchester, under the supervision of Professor Christopher Yau and Magnus Rattray, this multidisciplinary project aims to address a “grand challenge” within single-cell data science using artificial intelligence.

The PhD student will initially undertake two three-month mini-projects, working with local or external experimental collaborators in the cancer, developmental or inflammation biology, domain to gain an understanding of the computational problems in single-cell biology. Subsequently, the student will then take forward one project for the reminder of the PhD or synthesise a common computational modelling challenge relevant to both as the subject of further study. Research challenges could include developing artificial intelligence-driven models of cancer evolution, integration single cell multi-omics data sets, or creating novel model of spatiotemporal variation in molecular expression at the single cell level.

This project is suitable for a quantitative sciences graduate (e.g. mathematics, computer science, engineering, physics) with a substantial long-term interest in developing skills in artificial intelligence applied to biomedical problems. The student will receive training in modern statistical and machine learning methodologies and will have the opportunity to simultaneously work on both methodological and applied projects. They will learn and acquire biological and experimental knowledge with collaborators and show a willingness to develop knowledge in new areas.

https://cwcyau.github.io/

Entry Requirements:
Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

UK applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible. International applicants (including EU nationals) must ensure they meet the academic eligibility criteria (including English Language) as outlined before contacting potential supervisors to express an interest in their project. Eligibility can be checked via the University Country Specific information page (https://www.manchester.ac.uk/study/international/country-specific-information/).

If your country is not listed you must contact the Doctoral Academy Admissions Team providing a detailed CV (to include academic qualifications – stating degree classification(s) and dates awarded) and relevant transcripts.

Following the review of your qualifications and with support from potential supervisor(s), you will be informed whether you can submit a formal online application.

To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the MRC Doctoral Training Partnership (DTP) website www.manchester.ac.uk/mrcdtpstudentships

Funding Notes

Funding will cover UK tuition fees/stipend only. The University of Manchester aims to support the most outstanding applicants from outside the UK. We are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/
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