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PhD Studentship - Predicting Healthy Outcomes using Machine Learning of Longitudinal Data

University of Sheffield - Computer Science

Qualification Type: PhD
Location: Lower Kent Ridge Road - Singapore, Sheffield
Funding for: UK Students
Funding amount: £15,609 - please see advert
Hours: Full Time
Placed On: 11th October 2021
Closes: 1st November 2021

Since the advent of high-throughput genomics, proteomics and metabolomics, it has become common practice to collect and process longitudinal biological samples to follow the evolution of a disease or condition using molecular markers. One-dimension-at-a-time study designs for molecular data are inherently inefficient, resulting in the need for larger sample sizes than would otherwise be sufficient. In order to overcome this limitation, decrease the number of participants and effort needed for longitudinal population studies and increase the amount of information gained, we propose to develop a methodological framework for high-dimensional longitudinal data that will be tested on real-world data from birth cohorts in Singapore.

Dr Dennis Wang and Dr Mauricio Alvarez at The University of Sheffield (Sheffield) will be co-supervising a PhD candidate with Prof Michael Meaney (neuro-development), Dr Jonathan Huang (biostatistics) at the Singapore Institute for Clinical Sciences (SICS) to develop data analytics toolkits for longitudinal molecular data from birth cohorts.

The theoretical basis and prototyping for the methodology will be developed in Sheffield in collaboration with a postdoctoral fellow. The PhD candidate will test the methodology on real-world datasets from the S-PRESTO and GUSTO studies, and enable the open-source methodology to be used as a software toolkit for future studies in Singapore. This work is outlined in four work packages:

  1. Application of the longitudinal modelling method to describe repeated measures of DNA methylation in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO);
  2. Application of the longitudinal modelling method to repeated measures of metabolite profiles during preconception and course of pregnancy from the Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO) study.
  3. Critical comparison of population results obtained using existing and proposed methodology from GUSTO and S-PRESTO;
  4. Implementation of the new methodology as open-source software for future studies.

Required qualifications: Undergraduate degree in a quantitative discipline (physics, computer science, statistics, etc.) or a biomedical sciences degree with scientific programming experience.

Award details: Fully funded 4-year PhD programme.

Whilst the student is in Singapore:

Living allowance:

  • A monthly stipend of two thousand, seven hundred Singapore Dollars
  • (~£1,461) whilst in Singapore.
  • A one-off settling-in allowance; of one thousand Singapore dollars (~£530).
  • A one-time airfare allowance of one thousand five hundred Singapore dollars
  • (~£800).
  • One-time IT allowance of eight hundred Singapore dollars (~£425)
  • Medical insurance, Housing subsidy, Conference allowance.

Whilst in Sheffield, students receive primary fees (£4,500 in 21/22) and stipend at the UKRI rate (£15,609 in 2021/22). In addition, students may be able to claim up to £500 from Sheffield towards the costs of an airfare back to the UK whilst they are in Singapore in order to make a home visit.


Academic requirements: applicants should have, or expect to achieve a first or upper second class UK honours degree or equivalent qualifications gained outside the UK in an appropriate area of study. Allowed study options: applicants should be registering on their first year of study with the University for 2022/23 on an eligible programme of doctoral study. Awards are open to UK nationals, EU nationals (including non-settled), and others who have settled status in the UK.

Have strong referee reports from previous supervisors.

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