DRIVE-Health PhD Programme

Overview

We are looking to recruit outstanding graduates from a variety of backgrounds to a 3.5 year (or 3 year depending on funding source) PhD programme in Data-Driven Health to work on internationally-competitive research projects, equipping them to exploit excellence in medical and informatics research for improving the health of local and national patient populations; this call is for applications for a PhD studentship for October 2022 entry. The student will benefit from multi disciplinary supervision and opportunities for visits to our international partners.

DRIVE-Health studentships offer a generous stipend per annum, in line with the UK Research and Innovation (UKRI) rate. The Centre for Doctoral Training (CDT) will also provide funds for research project support – travel, conferences, etc.

Visit fees and funding webpages to find out more about bursaries, scholarships, grants, tuition fees, living expenses, student loans and other financial help available at King’s.

Academic Requirements

Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.

Application & Enquiries

Please apply via the King’s Apply website to the Programme: “DRIVE-Health: Centre for Doctoral Training in Data-Driven Health (MPhil/PhD)”.

For queries and suggestions for new project ideas please contact drivecdt@kcl.ac.uk in the first instance, who may put you in touch with a theme lead or an appropriate supervisor. All projects are list below.

Entry Requirements

English Language Requirements (Band D)

Based on the IELTS test scoring system, this programme requires that successful candidates achieve the following level of English before enrolling. Successful applicants’ offer letters will include information about when they must have achieved this standard.

  • Overall:  6.5
  • Listening: 6 
  • Speaking: 6 
  • Reading: 6 
  • Writing: 6

Visit our admissions webpages to view our English language entry requirements.

Personal Statement and Supporting Information

You will be asked to submit the following documents in order for your application to be considered:

  • Personal Statement (Yes)
    A personal statement is required. This can be entered directly into the online application form (maximum 4,000 characters) or uploaded as an attachment to the online application form if you have a longer personal statement (maximum 2 pages). Please include your top 3 project preferences in your personal statement.
  • Research Proposal
    A research proposal is not required if you are applying for our projects (you can apply for up to 3 projects). Simply enter the titles of the 3 preferred projects directly into the research proposal section of the online application form.
    If you are submitting your own project, a brief research proposal is required. You can enter the project proposal directly into the online application form (maximum 4,000 characters) or you have the option to upload it as an attachment to the application form if you have a longer research proposal. Maximum upload file size: 3MB.
  • Previous Academic Study (Yes)
    A copy (or copies) of your official academic transcript(s), showing the subjects studied and marks obtained. If you have already completed your degree, copies of your official degree certificate will also be required. Applicants with academic documents issued in a language other than English, will need to submit both the original and official translation of their documents.
  • Reference (Yes)
    Reference is required as part of an application. You can fill in the details of your referee into the online application form.
    When you submit your application, your referee will be sent a link to our King’s Referee Portal, where they can provide a reference.
    We will not accept references from personal email addresses (e.g. yahoo, hotmail, gmail or other similar public systems) and we are unable to accept references from family members or friends. Please use your referee’s official, professional email address.
  • Curriculum (Yes)
    Please include your CV (Resume) or evidence of professional registration as part of your application.

Funding

If you are applying for our DRIVE-Health Studentship, please tick “5. I am applying for a funding award or scholarship administered by King’s College London” in the funding section, and fill in the Award Scheme Code or Name box with “DRIVE-Health Studentships” inside the Award Scheme Code or Name box.

Important Date

Closing date for applications is 28th February 2022.


List of Projects for October 2022 Intake

IDProject TitleSupervisorCo-supervisor(s)
1Real-time pixel-level semantic interpretation of intraoperative optical coherence images for guided regenerative therapy deliveryDr Christos BergelesProf Tom Vercauteren, Dr Lyndon Da Cruz
2Deep Learning for the automated prediction of diabetic retinopathy progressionDr Christos BergelesProf Tim Jackson
3Disentangled knowledge representations for patient stratification in heart failureDr Andrew KingProf Reza Razavi, Dr Bram Ruijsin, Dr Esther Puyol-Antón
4Physiological stress regulation in adults with ADHD: a longitudinal remote monitoring study using a novel wearable deviceProf Jonna KuntsiProf Richard Dobson
5Classification and quantification of sexual minority group membership and the prediction of multiple health disparitiesDr Qazi RahmanDr Kate Rimes
6Prognostic predictors of outcome following psychological treatment for anxiety or depression: a longitudinal statistical learning approach using electronic health record dataProf Thalia EleyDr Ewan Carr
7The role of human mitochondria in complex disease riskDr Alan HodgkinsonProf Khuloud Al-Jamal, Dr Alfredo Iacoangeli
8Data science methodologies for drugs and targets in amyotrophic lateral sclerosisDr Sophia TsokaProf Khuloud Al-Jamal, Dr Jemeen Sreedharan
9Explaining the sexual dimorphism in Lupus through geneticsDr David MorrisProf Timothy Vyse
10Statistical and machine learning approaches to developing predictive tools for identifying risk of preterm birth and poor neonatal outcomeProf Rachel TribeDr Yanzhong Wang, Professor Andrew Shennan
11Characterisation of depression and anxiety symptoms utilising longitutindal speech and facial expression dataDr Nicholas CumminsDr Raquel Iniesta
12Does the gut microbiome predict disease better than the polygenic risk score?Prof Frances WilliamsDr Maxim Freydin
13A cost-effective deep learning approach for prediction of spatial transcriptomics from histological images in triple-negative breast cancerDr Mohammad Mahdi KarimiDr Sheeba Irshad
14Defining the molecular mechanisms in age-related hearing impairment (ARHI)Prof Frances WilliamsDr Maxim Freydin
15In situ contextual healthcare data capture with lightweight interaction techniquesDr Timothy NeateDr Vasa Curcin
16Unsupervised learning technique for relapse prediction in mhealth dataDr Nicholas CumminsDr Raquel Iniesta, Dr Srinivasan Vairavan
17Genetic and environmental regulation of adipose (fat) tissue multi-omics to understand physiological mechanisms and genetic risk of type 2 Diabetes and obesityDr Kerrin SmallDr Alan Hodgkinson
18Modelling ECG and PPG signals in hypertension and HFpEF: a database for in silico evaluation of health assessment algorithmsDr Jordi AlastrueyProf Phil Chowienczyk, Prof Steven Niederer
19Longitudinal multi-omic datasets to inform precision medicine and ageingDr Kerrin SmallDr Claire Steves
20Ketamine activity in treatment-resistant depression with history of early life stressDr Mario JuruenaProf Mitul Mehta
21Using Topological-Machine Learning Data Analysis for feature extraction and outcomes prediction in data from smartphones and wearable devicesDr Raquel IniestaDr Nicholas Cummins
22AI & future brain tumour treatment: Development of a panel of predictive immunotherapy biomarkers using MRI-based radiogenomic analysis of glioblastomasDr Thomas BoothDr Marc Modat, Dr Igor Vivanco, Prof Keyoumars Ashkan, Prof Richard Houlston
23Face-to-face or remote consultations for people with mental illness in secondary mental health careDr Mariana Pinto Da CostaProf Fiona Gaughran, Prof Robert Stewart
24Bayesian uncertainty quantification and propagation for personalized cardiac modellingDr Marina RiabizProf Steven Niederer, Prof Chris Oates
25Neurodevelopmental psychiatric conditions: a network analysis approach to the structure of psychopathologyProf Grainne McAlonanProfessor Federico Turkheimer
26Using machine-learning to predict pre-symptomatic intestinal inflammation in Crohn’s diseaseDr Natalie PrescottDr Raquel Iniesta
27Implementing machine learning methods to integrate radiological and pathological data to assess treatment response in oesophageal/gastro-oesophageal cancerDr Anita GrigoriadisProfessor Vicky Goh, Professor Gary Cook, Dr Kasia Owczarczyk
28Clinical decision support in management of thoracic malignancy through multi-omic data scienceDr Sophia TsokaProfessor Vicky Goh, Professor Gary Cook
29Vulnerability score for adolescents and young adults attending secondary careDr Ingrid WolfeDr Julia Forman
30Quality of care for adolescents and young adultsDr Ingrid WolfeDr Ann Hagell
31Comparative health systems performance assessment for children and young peopleDr Ingrid WolfeDr Dheepa Rajan, Dr Ann Hagell
32Evaluation of educational and healthcare outcomes and their interaction for children with chronic liver disease in the UKDr Marianne SamynDr Katie Harron
33A neurosymbolic approach to generating trust in Artificial Intelligence innovations in medicineDr Zina IbrahimDr Johnny Downs
34Use of artificial intelligence to evaluate effectiveness of polypharmacy in patients with depression-related multimorbidityDr Alex DreganDr Jayati Das-Munshi
35 A fully-quantifiable aetiopathogenic model for Parkinson’s and Overlap and Related Diseases to identify targets for personalized interventionProf Steven GilmourDr Sylvia Dobbs, Dr John Dobbs, Dr André Charlett