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PhD studentship in Artificial Intelligence for Precision Oncology


   Department of Bioengineering

   Thursday, July 14, 2022  Funded PhD Project (Students Worldwide)

About the Project

Environment

Imperial College London is consistently rated as one of the World’s leading universities (https://www.topuniversities.com/university-rankings/world-university-rankings/2022). Imperial’s Department of Bioengineering has a strong international profile and currently hosts about 230 PhD students, thus providing a supportive and stimulating environment to carry out a PhD. It was declared UK’s top department of bioengineering for research in the latest Research Excellence Framework. The department has a strong computational and theoretical modelling theme, and participates in Imperial networks such as the AI Network, the Centre for Drug Discovery Science and the CRUK Convergence Science Centre (https://www.convergencesciencecentre.ac.uk/) between Imperial (https://www.imperial.ac.uk/) and ICR (https://www.icr.ac.uk/).

Project

This project will investigate the application of artificial intelligence (AI) methods to precision oncology (PO). PO is a form of medicine that uses personal information to prevent, diagnose and treat cancers. Here we focus on the PO topic of how to predict which cancer patients will respond to a given drug treatment from the molecular profiles of their tumours. There are major methodological challenges limiting the potential of such AI models. Some are specific to this problem (e.g. how well a rare cancer type can be predicted using data from other types?). Other challenges are also found in other supervised learning problems (e.g. how to improve supervised learning when training with high-dimensional data?). This PhD project aims at making progress towards overcoming these challenges using both synthetic and real datasets.

Selection criteria - Essential

University degree/s awarded in an area directly relevant to the project.

Courses in the application of machine learning algorithms to scientific problems.

Excellent grades in first and/or master degrees, especially in their research projects, with a major focus on computational analysis of data.

Skilled in the implementation of Python or R scripts for scientific data analysis.

English language (https://www.imperial.ac.uk/study/pg/apply/requirements/english/). 

Selection criteria - Desirable

Research projects in the application of supervised learning to solve real-world problems in the context of biomedical research, especially precision medicine.

Some understanding of tumour profiling (omics), cancer biology and clinical oncology.

Ideally, trained on handling, integrating, processing and analysing molecular profiling data (e.g. DNA-seq, RNA-seq, miRNA-seq and/or DNA methylation microarrays).

Exposure to machine learning platforms (e.g. Scikit-Learn, Caret), and/or pharmaco-omic databases (e.g. GDC, GDSC).

Exposure to the application of machine learning algorithms to low-sample problems.

·Familiarity with clinical cancer pharmaco-omics databases (e.g. GDC).

How to apply

Candidates must send an email with their CV, grades for each held university degree and a covering letter (maximum two pages) to with subject line “PhD in AI for PO”. This letter must explain how they meet the essential selection criteria, which desirable selection criteria are also met and how this position would fit in their future career plans. This email must also state the names and emails of two scientists involved in assessing their academic performance, who are willing to provide a reference. Please also mention in the letter where did you see this position


Funding Notes

What we offer
The studentship covers living expenses at an enhanced rate (tax-free £17,609 per year) plus PhD registration fees (£26,600 per year) for three years, with the possibility of extending it to a 4th year.

References

The successful candidate will join the group of Dr Pedro Ballester at Imperial College and the PhD will be carried out under his direct supervision. These are some relevant papers from the group:
• https://doi.org/10.1371/journal.pone.0061318
• https://doi.org/10.3389/fgene.2019.01041
• https://www.nature.com/articles/s41698-021-00216-w
• https://doi.org/10.1093/bib/bbab450

How good is research at Imperial College London in Engineering?


Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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