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 email@example.com in the first instance, who may put you in touch with a theme lead or an appropriate supervisor.
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.
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 2020 Intake
|ID||Project Title||Lead Supervisor||Co-supervisor(s)|
|1||Bridging the gap between trials of health interventions and impact on patients: generalizing trial findings using electronic case records systems||Sabine Landau||Johnny Downs|
|2||Utilising 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 stroke||Yanzhong Wang||Charles Wolfe|
|3||Using advanced quantitative analyses on big data to determine the role of depression in outcomes of hip fracture: Interdisciplinary investigation||Salma Ayis||Katie Sheehan|
|4||Characterising Atrial Anisotropy and Fibrosis For Patient-Specific Models of Atrial Fibrillation Ablation||Steven Niederer||Martin Bishop|
|5||Improved Prediction Tools for Preeclampsia, Renal Dysfunction and Foetal Growth Restriction in Pregnancy, Using Advanced Statistical Techniques||Salma Ayis||Nick Kametas|
|6||CREED-N: Clinical Reporting of Electroencephalograms Enhanced through Deep Learning and NLP||James Teo||Mark Richardson, Jorge Cardoso|
|7||Longitudinal multi-omic datasets to inform precision medicine and ageing||Kerrin Small||Claire Steves|
|8||Extraction of novel signatures to improve the diagnosis of obstructive sleep apnoea||Manasi Nandi||Joerg Steier, Philip Aston, Ged Rafferty|
|9||A Computational Framework for Precision Medicine||Zina Ibrahim|
|10||Understanding Patient Heterogeneity through Machine Learning: A Study of Clozapine Adverse Drug Reactions||Zina Ibrahim||Richard Dobson|
|11||Machine Learning Techniques to Predict Deterioration of Patients with Cirrhosis in Hospital Wards||Zina Ibrahim||Mark McPhail|
|12||Using machine learning techniques to explore multimorbidity progression in patients with organic mental disorders||TBC||Rebecca Bendayan, Zina Ibrahim|
|13||Investigating links between severe mental illness and dementia using primary and secondary electronic health records.||TBC||Rebecca Bendayan, Robert Stewart|
|14||Developing deep learning models to predict youth mental health problems from parents' speech||Helen Fisher||Johnny Downs, Heidi Christensen|
|15||Deep learning for risk stratification of patients with liver cancers||Julia Schnabel||Cheng Fang|
|16||Moving the objective measurement of child emotions and behaviours from the lab to real world settings||Johnny Downs||Petr Slovak, Oya Celiktutan|
|17||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|
|18||Implications of clinical behaviours and design considerations for new generation AI-based clinical decision support tools||Vasa Curcin||Metadvice Ltd. (Industry Partner)|
|19||Advancing explainable human in the loop NLP analytics for clinical applications||Vasa Curcin||Metadvice Ltd. (Industry Partner)|
|20||Data Science Strategies for Cancer Immunotherapy Applications||Sophia Tsoka||Sophia Karagiannis|
|21||Emulating trials using EHR and Cogstack||Sabine Landau||James Teo, Richard Dobson, Dan Bean|
|22||Automated patient summarisation in electronic health records data||Richard Dobson||Dan Bean, Industry Partner (TBC)|
|23||High dimensional forecasting of patient flow in acute healthcare||Richard Dobson||James Teo, Dan Bean|
|24||Explaining variation in long-term healthcare costs and utilisation of Stroke: Comparing findings from alternative statistical methods||Julia Fox-Rushby||Marina Soley-Bori|
|25||Investigating links between socio-environmental factors and multimorbidity patterns in patients with severe mental illness.||TBC||Rebecca Bendayan, Jayati Das-Munshi|
|26||Predictive analytics for clinical decision support with application to Cardiovascular management.||Abdel Douiri||Vasa Curcin|
|27||Using Electronic Health Records to Identify Frailty in Inpatient Settings||Julie Whitney||James Teo, Fiona Gaughran|
|28||Using Electronic Health Records to Detect and Predict Inpatient Falls||Julie Whitney||James Teo / Fiona Gaughran|
|29||Applying digital to enhance ECG interpretation and response in mental health settings||Fiona Gaughran||Ajay Shah, Nicholas Gall|
|30||Federated AI - Accurate and privacy-preserving learning from distributed medical data||Jorge Cardoso||James Teo|
|31||Feasibility, acceptability and effectiveness of real time digital identification of clinical trial participants||Fiona Gaughran||Rob Stewart, Nabila Cruz|
|32||Stroke prevention in patients with atrial fibrillation (AF) and co-morbid physical and mental health problems||Fiona Gaughran||Mark Ashworth|
|33||Machine Learning for Disease Subtype Discovery||Mansoor Saqi||Richard Dobson|
|34||A whole-genome sequencing approach to advance precision medicine and study patient heterogeneity||Ammar Al-Chalabi||Alfredo Iacoangeli|
|35||Trajectories of anxiety and depression across development and treatment||Thalia Eley||Kimberley Goldsmith|
|36||Participatory Agent-Based Modelling of Emergency Department Patient Flow||Steffen Zschaler||TBC|
|37||Genotype to phenotype studies of inherited metabolic liver diseases using human iPSCs||Tamir Rashid||TBC|
|38||AI-based digitised pathology to identify subtypes in breast cancers||Anita Grigoriadis||Sarah Pinder, Pandu Raharja-Liu (Industry Partner)|
|39||A Real World Evidence approach to develop a better understanding of clinical outcomes of patients with myeoloproliferative neoplasms (MPNs)||Mieke Van Hemelrijck||Shahram Kordasti|
|40||Learning to Trust AI Models in Cardiology||Andrew King||Reza Razavi|
|41||Developing remote assessment and monitoring technology for ADHD||Jonna Kuntsi||Richard Dobson|
|42||Do longitudinal changes in the mitochondrial transcriptome modulate age-related disease risk?||Alan Hodgkinson||Kerrin Small|
|43||Urbanicity and psychosis- are cities bad for mental health? A large-scale data linkage study using electronic health records||Jayati Das-Munshi||Craig Morgan, Margaret Heslin|
|44||Neighbourhood / socioenvironmental predictors of outcomes in psychosis||Jayati Das-Munshi||Margaret Heslin|
|45||Investigating who are at most at harm from cannabis use and identifying actionable predictors of risk of transition to psychosis||Sagnik Bhattacharyya||Ben Carter|
|46||Positive symptoms and course of illness in people with first episode schizophrenia||Margaret Heslin||Rashmi Patel, Rob Stewart|
|47||Using multi-omic data for neuroendocrine cancer diagnostics and metastatic predictions||Rebecca Oakey||Cynthia Andoniadou, Louise Izatt|
|48||Revealing the molecular mechanisms of neurodegenerative diseases using the biological networks||Adil Mardinoglu|
|49||PMLCP : Precision Medicine approach for effective treatment of Liver Cancer Patients||Adil Mardinoglu|
|50||Multi-omics data integration for patient stratification in cancer clinical trials||Francesca Ciccarelli||Chris Yau|
|51||Linking patient outcomes to genomic data in the ICICLE and GLACIER trials to determine how inherited variation influence recurrence after in situ breast cancer||Elinor Sawyer||Marjanka Schmidt|
|52||Using machine learning to predict treatment pathways in end stage kidney disease (ESKD)||Mariam Molokhia||Claire Sharpe, Kateie Vinen, Kathleen Steinhöfel, Dorothea Nitsch|
|53||Can online markers of decision-making predict psychosis-onset in people at risk of psychosis?||Kelly Diederen||Tom Spencer|
|54||The Digital Twin in Heart Failure||Pablo Lamata||Gerry Carr-White|
|55||Can online assessment of speech predict psychosis-onset in people at clinical high risk of psychosis?||Kelly Diederen||Tom Spencer|
|56||IMPACT: Indirect measures of supragranular layer cortical thickness||Kelly Diederen||Tom Spencer|
|57||Data-driven analysis of the impact of Universal Credit on mental health usage using a novel data linkage||Sharon Stevelink||Sumithra Velupillai, Nicola Fear|
|58||Mental Health Consequences of Air Pollution||Ioannis Bakolis||Ian Mudway|
|59||Combining statistical and knowledge-based methods for clinical modelling of electronic health record text||Angus Roberts||Sumithra Velupillai, AIMES (Industry Partner)|
|60||Modelling patient mental, behavioural and somatic experiences using Natural Language Processing to improve risk detection of suicidal behaviour and self-harm||Rina Dutta||Sumithra Velupillai, AIMES (Industry Partner)|
|61||Integrating machine learning into stroke databases to detect and interpret variation in stroke care quality and outcomes||Jorge Cardoso||Abdel Douiri|
|62||Decision support in epidemic emergency care with application to COVID||Vasa Curcin||Jonathan Edgeworth|
|63||Modeling in-hospital transmissions during influenza and Covid-19 epidemics||Abdel Douiri||Jonathan Edgeworth|