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 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.
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 firstname.lastname@example.org in the first instance, who may put you in touch with a theme lead or an appropriate supervisor. All projects are list below.
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.
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.
Closing date for applications is 21st March 2021.
List of Projects for October 2021 Intake
|ID||Project Title||Lead Supervisor(s)||Co-supervisor(s)|
|1||Deep Learning for the automated prediction of diabetic retinopathy progression||Christos Bergeles||Timothy Jackson|
|2||Developing a novel approach for combining OMICs and clinical data for patient stratification in cancer||Mieke Van Hemelrijck||Shahram Kordasti|
|3||An investigation to explore the morbidity and mortality of prescribed opioids in the UK||Kim Wolff||Caroline Copeland|
|4||The immune mechanisms leading to long-COVID: a clinical and cellular study||Carmine M. Pariante||Frances Williams, Alessandra Borsini|
|5||Genetic and environmental epidemiology of ageing-related muscle weakening||Simon M Hughes||Nick Dand|
|6||Do lower blood pressure cut-offs in pregnancy identify women at greater risk of adverse maternal and perinatal outcomes?||Laura A. Magee||Peter von Dadelszen|
|7||Blood pressure (BP) variability and pregnancy outcomes||Laura A. Magee||Peter von Dadelszen|
|8||Disentangled knowledge representations for patient stratification in heart failure||Andrew King||Reza Razavi, Bram Ruijsink, Esther Puyol Anton|
|9||Women's Heart and Pregnancy Outcomes: Modified novel prediction tools||Salma Ayis||N Kametas|
|10||Applying machine learning techniques to multi-dimensional data to understand the role of mitochondria in complex disease||Alan Hodgkinson|
|11||A computational approach to study inherited cancer-using genetics to guide predicted outcomes||Rebecca Oakey||Cynthia Andoniadou, Louise Izatt|
|12||Social factors as predictors and outcomes of psychological treatment for anxiety and depression: a collaboration between KCL and Ieso Digital Health||Thalia Eley||Ewan Carr|
|13||AI-based image analyses to detect metastatic deposits in lymph node of head and neck patients||Anita Grigoriadis||Selvam Thavaraj|
|14||Narcotovigilance: Investigation of narcotic drug-drug/disease interactions||Caroline Copeland||Oana Cocarascu|
|15||Putting more Data Science in Implementation Science : Novel application of causal analysis methods to implementation science hybrid effectiveness-implementation clinical trials||Kimberley Goldsmith||Nick Sevdalis, Jane Sandall|
|16||Integrating electronic health records and genetics to dissect depression heterogeneity and treatment response||Cathryn Lewis||Jonathan Coleman|
|17||Developing and assessing novel remote monitoring technology for adults with attention deficit hyperactivity disorder||Jonna Kuntsi||Richard Dobson|
|18||A life course approach to characterize the immune and inflammatory modulation of the population cancer risk||Shahram Kordasti||Aida Santaolalla, Sophia N Karagiannis|
|19||Knowledge graphs for mining the Immune Mediated Inflammatory Disease (IMID) spectrum||Mansoor Saqi||Angus Roberts|
|20||Investigating the presence of shared genetic and pathophysiological mechanisms between Major Depressive Disorder and Alzheimer's Disease||Petroula Proitsi||Cathryn Lewis|
|21||Comprehensive genomic analysis and biomarker analysis of primary and recurrent head and neck squamous cell carcinoma from patients treated with immunotherapy||Anthony Kong||Anita Grigoriadis|
|22||Artificial Intelligence for the clinical and therapeutic stratification of psoriasis||Magnus Lynch||Catherine Smith, Satveer Mahil|
|23||New machine learning algorithms for risk stratification in glaucoma||Cynthia Yu-Wai-Man||Christopher Hammond|
|24||Machine Learning Techniques to Predict Deterioration of Patients with Cirrhosis in Hospital Wards||Mark McPhail||Zina Ibrahim|
|25||The impact of linguistic bias on models learned from labelled electronic health record text||Angus Roberts||Rina Dutta|
|26||Life Course Impact of Common Conditions Experienced In Childhood on Health Outcomes and Costs||Ingrid Wolfe||Marina Soley-Bori, Raghu Lingam|
|27||Identification of older adults at risk of post-discharge medication-related harm: implementation of a prognosic model in electronic health records||Jennifer Stevenson||Abdel Douiri, Kia-Chong Chua, Graham Davies|
|28||High throughput identification of risk phenotypes and mechanisms of disease deterioration in liver failure||Mark McPhail||Zina Ibrahim|
|29||Investigate the use of AI for longitudinal evaluation of prostate cancer patients on active surveillance (AS)||Sebastien Ourselin||Vicky Goh, Prokar Dasgupta, Michela Antonelli|
|30||The Digital Twin in Heart Failure towards its optimal longitudinal management||Pablo Lamata||Gerald Carr-white|
|31||A multi-omics approach to identify biochemical pathways associated with Alzheimer Disease||Petroula Proitsi||Daniel Stahl, Cristina Legido-Quigley|
|32||Can clinical decision making in oesophageal pre-cancer surveillance and therapy be automated? A study using natural language processing of gastrointestinal endoscopy reports||Angus Roberts||Sebastian Zeki,|
|33||A germline variant by somatic mutation (G×M) association study for Clonal Haematopoiesis||Mohammad Mahdi Karimi||Eric So|
|34||Use 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 Ferro||John Weinman|
|35||An artificial intelligence-powered prescribing aid for cardiovascular risk (CVD) prevention||Sophia Tsoka, Mohamed A Alhna, Henry Fok, Albert Ferro||John Weinman|
|36||Drivers and Mediators of Parkinson's and Overlap and Related Diseases: Black Box Deconstruction & Rational Rebuild||Steven Gilmour||John Dobbs, Sylvia Dobbs, André Charlett|
|37||Postnatal care following complicated pregnancy – healthcare utilisation and opportunities for health promotion||Sara L White, Laura A Magee||Angela Flynn, Lucilla Poston, Peter von Dadelszen|
|38||Improving Healthcare for All - Realising Value from Health Data||Ingrid Wolfe||Jeremy Yates|
|39||Improving outcomes for children with long-term conditions using a Learning Health System approach||Ingrid Wolfe||Elizabeth Cecil|
|40||Digital interventions for early detection and prevention of cardiovascular disease (CVD) through community-primary care partnerships||Mariam Molokhia, Seeromanie Harding||Salma Ayis, Clare Coultas|
|41||Using machine learning to predict treatment pathways in end stage kidney disease (ESKD)||Mariam Molokhia, Katie Vinen, Claire Sharpe||Kathleen Steinhöfel, Dorothea Nitsch|
|42||Towards a better understanding of the natural history of the inherited metabolic liver disease and cardiorespiratory comorbidities||Mariam Molokhia, Richard Thompson||Mary Bythell, Steven Hardy, Tamir Rashid|
|43||Mental health consequences of air pollution over the life course||Ioannis Bakolis||Helen Fisher, Ian Mudway|
|44||Bridging the gap between trials of health interventions and impact on patients: generalizing trial findings using electronic case records systems||Sabine Landau||Johnny Downs|
|45||Trialling personalised treatment recommendations within IAPT services using causal inference and artificial intelligence||Sabine Landau||Jorge Cardoso|
|46||Moving objective measurement of child emotions and behaviours from the lab to real world settings||Johnny Downs||Petr Slovak, Oya Celiktutan|
|47||Design, development and validation of wearable system to collect in-situ measurements of mood, anxiety and stress for children aged 6-12 years||Petr Slovak||Johnny Downs, Edmund Sonuga-Barke|