PhD Studentship: Using AI and Big Data to Identify a Personalised Medicine Regime in Individual Tumours

Project Title: Using AI and big data to identify a personalised medicine regime in individual tumours

Brief Description of the Project: The ultimate goal in cancer treatment is to identify the therapeutic vulnerabilities of a patient’s tumour and use this to design a personalised medicine regime. 

The recent cost reduction in genomic technologies, has allowed extensive genomic analysis of clinical samples but for most tumour types, we lack the ability to translate these data into a successful therapeutic strategy. The Pearl bioinformatics laboratory have therefore developed a suite of artificial intelligence (AI) algorithms that use cancer genomic and other ‘big’ data sets to predict druggable vulnerabilities in cancer cells.

The aim of this PhD project is to improve the range and the accuracy of our current algorithms.  These includes implementing novel ‘state of the art’ AI methods in combination with newer, larger datasets.  These algorithms will be validated experimentally in the Hocheggar lab.

The student will be trained in programming, bioinformatics, big data and data science, cancer biology and therapeutics, in the Pearl bioinformatics laboratory. Training in cancer cell culture, cell proliferation analysis, and RNA interference technology for target validation, will be provided in the Hochegger Lab. 

How to apply:

Please submit a formal application using the online admissions portal attaching a CV, degree transcripts and certificates, personal statement and two academic references.

On the application system select Programme of Study – PhD Biochemistry. Please state the project title under funding and the supervisor’s name where required.

The ideal candidate would have a first degree in Life Sciences (e.g. Biochemistry, Biomedical Sciences etc) and a Master’s degree in a computation discipline (e.g. Bioinformatics, Data Science etc) or a proven ability in computer programming.  Alternatively, the project would suit a candidate from a mathematical or computation discipline (e.g. Computer Science, Maths, Statistics, Data Science) who is happy to be trained in laboratory skills.

Eligible applicants will hold a 2:1 BSc in a relevant subject.

Candidates for whom English is not their first language will require an IELTS score of 6.5 overall, with not less than 6.0 in any section.

For enquiries about the application process, contact Emma Chorley: lifesci-rec@sussex.ac.uk

For enquiries about the project, contact Dr Frances Pearl: f.pearl@sussex.ac.uk

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