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Development of a risk model for prediction of childhood asthma by data integration

  • Full or part time
    Dr F Rezwan
  • Application Deadline
    Wednesday, August 01, 2018
  • Funded PhD Project (UK Students Only)
    Funded PhD Project (UK Students Only)

About This PhD Project

Project Description

Faculty of Medicine

Main Supervisor:
Faisal I Rezwan

Other members of the supervisory team:
John W Holloway, S Hasan Arshad

Duration of the award: 36 months full time

Project description:
Applications are invited for a full-time PhD research positions at the Faculty of Medicine, University of Southampton, UK. The PhD positions will focus on application of data integration and machine learning in health and ‘omic’ data for clinical prediction.

This project will initiate a pilot study to implement data integration technique to build a risk model for predicting the probability of children in risk of developing asthma at 6-7 years of age in a birth cohort, and further to develop a prototype of the tool available via worldwide web. This will be done utilising longitudinal information of their prenatal and early life exposures along with ‘omic’ data. To explore and validate the risk factors (clinical, epidemiological, genetics and environmental) associated with childhood asthma at 6-7 years of age we will use rich data set available from the Isle of Wight Third Generation Cohort. We aim to estimate risk score for each risk factor and generate a predictive risk model for individual risk stratification of childhood asthma aforementioned ages, taking account the combined effects of the factors by data integration. Further, an online interactive tool will be implemented for personalised risk prediction based on the risk score developed and disseminate freely and widely to the population, patients, and health care providers. This work will provide an evidence base for risk identification at individual level that can identify future preventive and therapeutic strategies and policies. This would allow us to assess the potential impact of child health strategies to reduce exposure to key risk factors for childhood asthma.

The PhD studentship applications aim to train a talented postgraduate in key areas of genomics and computational modelling of ‘omics data. The student will: apply programming skills to manipulate and process raw data through relevant pipelines; learn and apply critical procedures for data curation and quality control; generate and collate key output data from genomics, transcriptomics, and methylomics and this will interface with health informatics to harmonise integrate data for the training and testing of mathematical and computational models; interpret and translate key findings back to clinical research staff and the wider scientific community. This will be performed under the guidance of senior academics experienced in genomic informatics and advanced bioinformatics and an internationally recognised clinical expert in asthma.

Please contact:
Faisal I Rezwan ()

Person Specification: See document link below.
https://jobs.soton.ac.uk/Upload/vacancies/files/18626/USE%2003%20Doctoral%20Researcher%20Person%20Specification_UoS_FoM_PhD.DOCX

The successful candidate is expected to have either excellent qualifications in bioinformatics or a firm mathematical background, with a degree in a quantitative discipline such as mathematics, physical sciences, or computer sciences coupled with a strong interest in genetics and genomics of human disease. The project encompasses mainly “dry lab” experimentation and computational modelling but requires excellent communication skills to join a multidisciplinary team. Complementary skills in statistical analysis and computer programming will be a distinct advantage. A genuine excitement for, and interest in mathematical modelling of biological processes to address complex biomedical questions is essential for this project.

The successful candidate is likely to have the following qualifications:

• A 1stor 2:1 degree in a relevant discipline and/or second degree with a related Masters

Administrative contact and how to apply:
Please complete the University’s online application form, which you can find at
https://studentrecords.soton.ac.uk/BNNRPROD/bzsksrch.P_Login?pos=7209&majr=7209&term=201819

You should enter Dr. Faisal I Rezwan as your proposed supervisor. To support your application provide an academic CV (including contact details of two referees), official academic transcripts and a personal statement (outlining your suitability for the studentship, what you hope to achieve from the PhD and your research experience to date).

Informal enquiries relating to the project or candidate suitability should be directed to : Dr. Faisal I Rezwan ().

Closing date: 01/08/2018
Interview date: 15/08/2018

Funding Notes

Due to funding restrictions this position is only open to UK applicants. This is a fully funded for 3 years project covering fees and stipend.


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