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. The Centre for Doctoral Training (CDT) will also provide funds for research project support – travel, conferences, etc.

Costs for PhD fees (UK/EU applicants) are covered by the CDT. We also have a limited number of full fee waivers for international applicants. We welcome applications from international applicants if they are able to top up the fees themselves.

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)”.

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.

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 (Yes)
    A research proposal is required. You can enter a brief synopsis of your research proposal directly into the online application form (maximum 4,000 characters) and have the option to upload it as an attachment to the online application form if you have a longer research proposal. If you do not wish to submit your own project simply duplicate the project preferences in the research proposal section. 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.
  • Other (Optional)
    You may wish to include a CV (Resume) or evidence of professional registration as part of your application.


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 Dates

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

List of Projects for First Round of Application 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 disordersTBCRebecca Bendayan, Zina Ibrahim
13Investigating links between severe mental illness and dementia using primary and secondary electronic health records.TBCRebecca Bendayan, Robert 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 years.Petr SlovakJohnny Downs, Edmund Sonuga-Barke
18Implications of clinical behaviours and design considerations for new generation AI-based clinical decision support toolsVasa CurcinMetadvice Ltd. (Industry Partner)
19Advancing explainable human in the loop NLP analytics for clinical applicationsVasa CurcinMetadvice Ltd. (Industry Partner)
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 illness.TBCRebecca Bendayan, Jayati Das-Munshi
26Predictive analytics for clinical decision support with application to Cardiovascular management.Abdel 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 participants Fiona 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 heterogeneityAmmar Al-ChalabiAlfredo Iacoangeli
35Trajectories of anxiety and depression across development and treatmentThalia EleyKimberley Goldsmith
36Participatory Agent-Based Modelling of Emergency Department Patient FlowSteffen ZschalerTBC
37Genotype to phenotype studies of inherited metabolic liver diseases using human iPSCsTamir RashidTBC
38AI-based digitised pathology to identify subtypes in breast cancersAnita GrigoriadisSarah Pinder, Pandu Raharja-Liu (Industry Partner)
39A Real World Evidence approach to develop a better understanding of clinical outcomes of patients with myeoloproliferative neoplasms (MPNs)Mieke Van HemelrijckShahram Kordasti
40Learning to Trust AI Models in CardiologyAndrew KingReza Razavi
41Developing remote assessment and monitoring technology for ADHDJonna KuntsiRichard Dobson
42Do longitudinal changes in the mitochondrial transcriptome modulate age-related disease risk?Alan HodgkinsonKerrin Small
43Urbanicity and psychosis- are cities bad for mental health? A large-scale data linkage study using electronic health recordsJayati Das-MunshiCraig Morgan, Margaret Heslin
44Neighbourhood / socioenvironmental predictors of outcomes in psychosisJayati Das-MunshiMargaret Heslin
45Investigating who are at most at harm from cannabis use and identifying actionable predictors of risk of transition to psychosisSagnik BhattacharyyaBen Carter
46Positive symptoms and course of illness in people with first episode schizophreniaMargaret HeslinRashmi Patel, Rob Stewart
47Using multi-omic data for neuroendocrine cancer diagnostics and metastatic predictionsRebecca OakeyCynthia Andoniadou, Louise Izatt
48Revealing the molecular mechanisms of neurodegenerative diseases using the biological networksAdil Mardinoglu
49PMLCP : Precision Medicine approach for effective treatment of Liver Cancer PatientsAdil Mardinoglu
50Multi-omics data integration for patient stratification in cancer clinical trialsFrancesca CiccarelliChris Yau
51Linking patient outcomes to genomic data in the ICICLE and GLACIER trials to determine how inherited variation influence recurrence after in situ breast cancerElinor SawyerMarjanka Schmidt
52Using machine learning to predict treatment pathways in end stage kidney disease (ESKD)Mariam MolokhiaClaire Sharpe, Kateie Vinen, Kathleen Steinhöfel, Dorothea Nitsch
53Can online markers of decision-making predict psychosis-onset in people at risk of psychosis?Kelly DiederenTom Spencer
54The Digital Twin in Heart FailurePablo LamataGerry Carr-White
55Can online assessment of speech predict psychosis-onset in people at clinical high risk of psychosis?Kelly DiederenTom Spencer
56IMPACT: Indirect measures of supragranular layer cortical thicknessKelly DiederenTom Spencer
57Data-driven analysis of the impact of Universal Credit on mental health usage using a novel data linkageSharon StevelinkSumithra Velupillai, Nicola Fear
58Mental Health Consequences of Air PollutionIoannis BakolisIan Mudway
59Combining statistical and knowledge-based methods for clinical modelling of electronic health record textAngus RobertsSumithra Velupillai, AIMES (Industry Partner)
60Modelling patient mental, behavioural and somatic experiences using Natural Language Processing to improve risk detection of suicidal behaviour and self-harmRina DuttaSumithra Velupillai, AIMES (Industry Partner)
61Integrating machine learning into stroke databases to detect and interpret variation in stroke care quality and outcomesJorge CardosoAbdel Douiri
62Decision support in epidemic emergency care with application to COVIDVasa CurcinJonathan Edgeworth
63Modeling in-hospital transmissions during influenza and Covid-19 epidemicsAbdel DouiriJonathan Edgeworth