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Development of machine learning-based methodology to study patterns in medical data (in application to COVID-19)


Key facts

Type of research degree
Application deadline
Ongoing deadline
Country eligibility
International (open to all nationalities, including the UK)
Competition funded
Professor Charles Taylor and Dr Sofya Titarenko
Additional supervisors
Professor Ann Morgan; Dr Mark Iles
School of Mathematics
Research groups/institutes
Modern applied statistics, Statistics
<h2 class="heading hide-accessible">Summary</h2>

In the era of big data analytics, statistical and machine learning (ML) tools are widely used to study the relationships and patterns in data. <br /> <br /> Large public datasets form a unique resource for developing novel statistical and ML-based methods with the potential to support important clinical applications.<br /> <br /> The COVID-19 pandemic caused by SARS-CoV-2 made a significant socio-economic impact on countries worldwide. However, there is still no complete understanding of the main contributors to the severity of COVID-19 and their interdependence. Some of the most challenging to include are co-morbidities, sociodemographic status, lifestyle factors and molecular data, such as polygenic risk score (PRS). PRS quantifies the combined effect of multiple common genetic variants of moderate effect.<br /> <br /> ML tools are extensively used in health bioinformatics to identify important risk factors in data and contribute to diagnostics/precision medicine. However, the methods are extremely data-dependent and do not work uniformly well for all cases. For example, many ML models assume linear relationships between variables. While this assumption is correct for some datasets, many complex data include non-linearity, which is very difficult to capture. Data can also be highly dimensional (including many variables), making the modelling even more challenging. <br /> <br /> This project focuses on developing novel ML-based methods to capture complex and non-linear relationships between the variables. In addition, the student will look for new patterns of lifestyle and genetic factors linked to clinical records associated with the severity of COVID-19. <br /> <br /> The student will work in a multidisciplinary team, collaborating strongly with the School of Medicine. The student will get an exciting opportunity to work with real-life data (UK Biobank data), which are rich in demographic information and clinical records, including the history of patients' medication and genetic data.

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Statistics FT,</strong></em>&nbsp;in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Development of machine learning-based methodology to study patterns in medical data (in application to COVID-19)</strong></em> as well as&nbsp;<a href="">Dr Sofya Titarenko</a> as your proposed supervisor&nbsp;and in the finance section, please state clearly&nbsp;<em><strong>the funding that you are applying for, if you are self-funding or externally sponsored</strong></em>.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p class="MsoNoSpacing">Applications will be considered on an ongoing basis. &nbsp;Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of 3 April 2024 for Leeds Opportunity Research Scholarship or 8 April 2024 for Leeds Doctoral Scholarship:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p class="MsoNoSpacing"><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK</strong>&nbsp;&ndash;&nbsp;The&nbsp;<a href="">Leeds Doctoral Scholarships</a>&nbsp;(open from October 2023) and&nbsp;<a href="">Leeds Opportunity Research Scholarship</a>&nbsp;(open from October 2023) are available to UK applicants. <a href="">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK</strong> &ndash;&nbsp;The&nbsp;<a href="">Leeds Marshall Scholarship</a>&nbsp;is available to support US citizens. <a href="">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Important:</strong>&nbsp; Any costs associated with your arrival at the University of Leeds to start your PhD including flights, immigration health surcharge/medical insurance and Visa costs are not covered under this studentship.</p> <p>Please refer to the&nbsp;<a href="">UKCISA</a>&nbsp;website for&nbsp;information regarding Fee Status for Non-UK Nationals.</p> <p>&nbsp;</p>

<h2 class="heading">Contact details</h2>

<p>For general enquiries about applications, contact Doctoral College Admissions by email to&nbsp;<a href=""></a><br /> For questions about the research project, contact Dr Sofya Titarenko by email to&nbsp;<a href=""></a></p>

<h3 class="heading heading--sm">Linked research areas</h3>