DRIVE-Health PhD Programme


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 PhD studentships for October 2020 entry. Students will benefit from multi disciplinary supervision and opportunities for visits to our international partners.

DRIVE-Health studentships offer a generous stipend of £17,520 per annum for the 2020/21 academic year; costs for PhD fees (UK/EU applicants) are covered by the CDT, but overseas applicants can apply if they are able to top up the fees themselves. The 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.

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.

Exemplar projects are list below. Applicants are encouraged to contact the supervisors to arrange an informal discussion before applying and to get the full project details. There will be opportunity for successful candidates to further develop project proposals with supervisors. Please apply via the King’s Apply website to the Programme: “DRIVE-Health: Centre for Doctoral Training in Data-Driven Health (MPhil/PhD)” and include your top 3 project preferences in your personal statement.

For queries and suggestions for new project ideas please contact in the first instance, who may put you in touch with a theme lead or an appropriate supervisor.

Important Dates

The closing date for applications under this scheme is Sunday 19th April 2020. Interviews for shortlisted candidates have been provisionally scheduled for the week of 11 May 2020. Successful applicants are expected to take up their studentships in September 2020.

List of Projects for 2020 Intake

IDProject TitleLead SupervisorCo-supervisor(s)
1Bridging the gap between trials of health interventions and impact on patients: generalizing trial findings using electronic case records systemsSabine LandauJohnny Downs
2Utilising group-based trajectory modelling to explore patterns of long-term outcome trajectories in patients with haemorrhagic stroke, their associated predictors, and their differences from those in patients with ischaemic strokeYanzhong WangCharles Wolfe
3Using advanced quantitative analyses on big data to determine the role of depression in outcomes of hip fracture: Interdisciplinary investigationSalma AyisKatie Sheehan
4Characterising Atrial Anisotropy and Fibrosis For Patient-Specific Models of Atrial Fibrillation AblationSteven NiedererMartin Bishop
5Improved Prediction Tools for Preeclampsia, Renal Dysfunction and Foetal Growth Restriction in Pregnancy, Using Advanced Statistical TechniquesSalma AyisNick Kametas
6CREED-N: Clinical Reporting of Electroencephalograms Enhanced through Deep Learning and NLPJames TeoMark Richardson, Jorge Cardoso
7Longitudinal multi-omic datasets to inform precision medicine and ageingKerrin SmallClaire Steves
8Extraction of novel signatures to improve the diagnosis of obstructive sleep apnoeaManasi NandiJoerg Steier, Philip Aston, Ged Rafferty
9A Computational Framework for Precision MedicineZina Ibrahim
10Understanding Patient Heterogeneity through Machine Learning: A Study of Clozapine Adverse Drug ReactionsZina IbrahimRichard Dobson
11Machine Learning Techniques to Predict Deterioration of Patients with Cirrhosis in Hospital WardsZina IbrahimMark McPhail
12Using machine learning techniques to explore multimorbidity progression in patients with organic mental disordersRebecca BendayanZina Ibrahim
13Investigating links between severe mental illness and dementia using primary and secondary electronic health recordsRebecca BendayanRobert Stewart
14Developing deep learning models to predict youth mental health problems from parents' speechHelen FisherJohnny Downs, Heidi Christensen
15Deep learning for risk stratification of patients with liver cancersJulia SchnabelCheng Fang
16Moving the objective measurement of child emotions and behaviours from the lab to real world settingsJohnny DownsPetr Slovak, Oya Celiktutan
17Design, 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
18Implications of clinical behaviours and design considerations for new generation AI-based clinical decision support toolsVasa Curcin
19Applications of Natural Language Processing for extraction and organisation of medical data to support clinical knowledge networks and clinical decision platformsVasa Curcin
20Data Science Strategies for Cancer Immunotherapy ApplicationsSophia TsokaSophia Karagiannis
21Emulating trials using EHR and CogstackSabine LandauJames Teo, Richard Dobson, Dan Bean
22Automated patient summarisation in electronic health records dataRichard DobsonDan Bean, Industry Partner (TBC)
23High dimensional forecasting of patient flow in acute healthcareRichard DobsonJames Teo, Dan Bean
24Explaining variation in long-term healthcare costs and utilisation of Stroke: Comparing findings from alternative statistical methodsJulia Fox-RushbyMarina Soley-Bori
25Investigating links between socio-environmental factors and multimorbidity patterns in patients with severe mental illnessRebecca BendayanJayati Das-Munshi
26Predictive analytics for clinical decision support with application to Cardiovascular managementAbdel DouiriVasa Curcin
27Using Electronic Health Records to Identify Frailty in Inpatient SettingsJulie WhitneyJames Teo, Fiona Gaughran
28Using Electronic Health Records to Detect and Predict Inpatient FallsJulie WhitneyJames Teo / Fiona Gaughran
29Applying digital to enhance ECG interpretation and response in mental health settingsFiona GaughranAjay Shah, Nicholas Gall
30Federated AI - Accurate and privacy-preserving learning from distributed medical dataJorge CardosoJames Teo
31Feasibility, acceptability and effectiveness of real time digital identification of clinical trial participantsFiona GaughranRob Stewart, Nabila Cruz
32Stroke prevention in patients with atrial fibrillation (AF) and co-morbid physical and mental health problemsFiona GaughranMark Ashworth
33Machine Learning for Disease Subtype DiscoveryMansoor SaqiRichard Dobson
34A whole-genome sequencing approach to advance precision medicine and study patient heterogeneityAlfredo IacoangeliAmmar Al-Chalabi