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 2021 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 21st March 2021.


List of Projects for October 2021 Intake

IDProject TitleLead Supervisor(s)Co-supervisor(s)
1Deep Learning for the automated prediction of diabetic retinopathy progressionChristos BergelesTimothy Jackson
2Developing a novel approach for combining OMICs and clinical data for patient stratification in cancerMieke Van HemelrijckShahram Kordasti
3An investigation to explore the morbidity and mortality of prescribed opioids in the UKKim WolffCaroline Copeland
4The immune mechanisms leading to long-COVID: a clinical and cellular studyCarmine M. ParianteFrances Williams, Alessandra Borsini
5Genetic and environmental epidemiology of ageing-related muscle weakeningSimon M HughesNick Dand
6Do lower blood pressure cut-offs in pregnancy identify women at greater risk of adverse maternal and perinatal outcomes?Laura A. MageePeter von Dadelszen
7Blood pressure (BP) variability and pregnancy outcomesLaura A. MageePeter von Dadelszen
8Disentangled knowledge representations for patient stratification in heart failureAndrew KingReza Razavi, Bram Ruijsink, Esther Puyol Anton
9Women's Heart and Pregnancy Outcomes: Modified novel prediction toolsSalma AyisN Kametas
10Applying machine learning techniques to multi-dimensional data to understand the role of mitochondria in complex diseaseAlan Hodgkinson
11A computational approach to study inherited cancer-using genetics to guide predicted outcomesRebecca OakeyCynthia Andoniadou, Louise Izatt
12Social factors as predictors and outcomes of psychological treatment for anxiety and depression: a collaboration between KCL and Ieso Digital HealthThalia EleyEwan Carr
13AI-based image analyses to detect metastatic deposits in lymph node of head and neck patientsAnita GrigoriadisSelvam Thavaraj
14Narcotovigilance: Investigation of narcotic drug-drug/disease interactionsCaroline CopelandOana Cocarascu
15Putting more Data Science in Implementation Science : Novel application of causal analysis methods to implementation science hybrid effectiveness-implementation clinical trialsKimberley GoldsmithNick Sevdalis, Jane Sandall
16Integrating electronic health records and genetics to dissect depression heterogeneity and treatment response Cathryn LewisJonathan Coleman
17Developing and assessing novel remote monitoring technology for adults with attention deficit hyperactivity disorder Jonna KuntsiRichard Dobson
18A life course approach to characterize the immune and inflammatory modulation of the population cancer risk Shahram KordastiAida Santaolalla, Sophia N Karagiannis
19Knowledge graphs for mining the Immune Mediated Inflammatory Disease (IMID) spectrum Mansoor SaqiAngus Roberts
20Investigating the presence of shared genetic and pathophysiological mechanisms between Major Depressive Disorder and Alzheimer's DiseasePetroula ProitsiCathryn Lewis
21Comprehensive genomic analysis and biomarker analysis of primary and recurrent head and neck squamous cell carcinoma from patients treated with immunotherapy Anthony KongAnita Grigoriadis
22Artificial Intelligence for the clinical and therapeutic stratification of psoriasisMagnus LynchCatherine Smith, Satveer Mahil
23New machine learning algorithms for risk stratification in glaucomaCynthia Yu-Wai-ManChristopher Hammond
24Machine Learning Techniques to Predict Deterioration of Patients with Cirrhosis in Hospital WardsMark McPhailZina Ibrahim
25The impact of linguistic bias on models learned from labelled electronic health record textAngus RobertsRina Dutta
26Life Course Impact of Common Conditions Experienced In Childhood on Health Outcomes and CostsIngrid WolfeMarina Soley-Bori, Raghu Lingam
27Identification of older adults at risk of post-discharge medication-related harm: implementation of a prognosic model in electronic health recordsJennifer StevensonAbdel Douiri, Kia-Chong Chua, Graham Davies
28High throughput identification of risk phenotypes and mechanisms of disease deterioration in liver failureMark McPhailZina Ibrahim
29Investigate the use of AI for longitudinal evaluation of prostate cancer patients on active surveillance (AS) Sebastien OurselinVicky Goh, Prokar Dasgupta, Michela Antonelli
30The Digital Twin in Heart Failure towards its optimal longitudinal managementPablo LamataGerald Carr-white
31A multi-omics approach to identify biochemical pathways associated with Alzheimer Disease Petroula ProitsiDaniel Stahl, Cristina Legido-Quigley
32Can clinical decision making in oesophageal pre-cancer surveillance and therapy be automated? A study using natural language processing of gastrointestinal endoscopy reportsAngus RobertsSebastian Zeki,
33A germline variant by somatic mutation (G×M) association study for Clonal HaematopoiesisMohammad Mahdi KarimiEric So
34Use of power of machine learning (ML) to predict and improve medication adherence in patients with cardiovascular disease (CVD)Sophia Tsoka, Mohamed A Alhna, Henry Fok, Albert FerroJohn Weinman
35An artificial intelligence-powered prescribing aid for cardiovascular risk (CVD) prevention Sophia Tsoka, Mohamed A Alhna, Henry Fok, Albert FerroJohn Weinman
36Drivers and Mediators of Parkinson's and Overlap and Related Diseases: Black Box Deconstruction & Rational RebuildSteven GilmourJohn Dobbs, Sylvia Dobbs, André Charlett
37Postnatal care following complicated pregnancy – healthcare utilisation and opportunities for health promotionSara L White, Laura A MageeAngela Flynn, Lucilla Poston, Peter von Dadelszen
38Improving Healthcare for All - Realising Value from Health DataIngrid Wolfe Jeremy Yates
39Improving outcomes for children with long-term conditions using a Learning Health System approachIngrid WolfeElizabeth Cecil
40Digital interventions for early detection and prevention of cardiovascular disease (CVD) through community-primary care partnershipsMariam Molokhia, Seeromanie HardingSalma Ayis, Clare Coultas
41Using machine learning to predict treatment pathways in end stage kidney disease (ESKD)Mariam Molokhia, Katie Vinen, Claire SharpeKathleen Steinhöfel, Dorothea Nitsch
42Towards a better understanding of the natural history of the inherited metabolic liver disease and cardiorespiratory comorbiditiesMariam Molokhia, Richard Thompson Mary Bythell, Steven Hardy, Tamir Rashid
43Mental health consequences of air pollution over the life courseIoannis BakolisHelen Fisher, Ian Mudway
44Bridging the gap between trials of health interventions and impact on patients: generalizing trial findings using electronic case records systemsSabine LandauJohnny Downs
45Trialling personalised treatment recommendations within IAPT services using causal inference and artificial intelligenceSabine LandauJorge Cardoso
46Moving objective measurement of child emotions and behaviours from the lab to real world settingsJohnny DownsPetr Slovak, Oya Celiktutan
47Design, development and validation of wearable system to collect in-situ measurements of mood, anxiety and stress for children aged 6-12 yearsPetr SlovakJohnny Downs, Edmund Sonuga-Barke