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 2022 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 28th February 2022.
List of Projects for October 2022 Intake
| ID | Project Title | Supervisor | Co-supervisor(s) |
|---|---|---|---|
| 1 | Real-time pixel-level semantic interpretation of intraoperative optical coherence images for guided regenerative therapy delivery | Dr Christos Bergeles | Prof Tom Vercauteren, Dr Lyndon Da Cruz |
| 2 | Deep Learning for the automated prediction of diabetic retinopathy progression | Dr Christos Bergeles | Prof Tim Jackson |
| 3 | Disentangled knowledge representations for patient stratification in heart failure | Dr Andrew King | Prof Reza Razavi, Dr Bram Ruijsin, Dr Esther Puyol-Antón |
| 4 | Physiological stress regulation in adults with ADHD: a longitudinal remote monitoring study using a novel wearable device | Prof Jonna Kuntsi | Prof Richard Dobson |
| 5 | Classification and quantification of sexual minority group membership and the prediction of multiple health disparities | Dr Qazi Rahman | Dr Kate Rimes |
| 6 | Prognostic predictors of outcome following psychological treatment for anxiety or depression: a longitudinal statistical learning approach using electronic health record data | Prof Thalia Eley | Dr Ewan Carr |
| 7 | The role of human mitochondria in complex disease risk | Dr Alan Hodgkinson | Prof Khuloud Al-Jamal, Dr Alfredo Iacoangeli |
| 8 | Data science methodologies for drugs and targets in amyotrophic lateral sclerosis | Dr Sophia Tsoka | Prof Khuloud Al-Jamal, Dr Jemeen Sreedharan |
| 9 | Explaining the sexual dimorphism in Lupus through genetics | Dr David Morris | Prof Timothy Vyse |
| 10 | Statistical and machine learning approaches to developing predictive tools for identifying risk of preterm birth and poor neonatal outcome | Prof Rachel Tribe | Dr Yanzhong Wang, Professor Andrew Shennan |
| 11 | Characterisation of depression and anxiety symptoms utilising longitutindal speech and facial expression data | Dr Nicholas Cummins | Dr Raquel Iniesta |
| 12 | Does the gut microbiome predict disease better than the polygenic risk score? | Prof Frances Williams | Dr Maxim Freydin |
| 13 | A cost-effective deep learning approach for prediction of spatial transcriptomics from histological images in triple-negative breast cancer | Dr Mohammad Mahdi Karimi | Dr Sheeba Irshad |
| 14 | Defining the molecular mechanisms in age-related hearing impairment (ARHI) | Prof Frances Williams | Dr Maxim Freydin |
| 15 | In situ contextual healthcare data capture with lightweight interaction techniques | Dr Timothy Neate | Dr Vasa Curcin |
| 16 | Unsupervised learning technique for relapse prediction in mhealth data | Dr Nicholas Cummins | Dr Raquel Iniesta, Dr Srinivasan Vairavan |
| 17 | Genetic and environmental regulation of adipose (fat) tissue multi-omics to understand physiological mechanisms and genetic risk of type 2 Diabetes and obesity | Dr Kerrin Small | Dr Alan Hodgkinson |
| 18 | Modelling ECG and PPG signals in hypertension and HFpEF: a database for in silico evaluation of health assessment algorithms | Dr Jordi Alastruey | Prof Phil Chowienczyk, Prof Steven Niederer |
| 19 | Longitudinal multi-omic datasets to inform precision medicine and ageing | Dr Kerrin Small | Dr Claire Steves |
| 20 | Ketamine activity in treatment-resistant depression with history of early life stress | Dr Mario Juruena | Prof Mitul Mehta |
| 21 | Using Topological-Machine Learning Data Analysis for feature extraction and outcomes prediction in data from smartphones and wearable devices | Dr Raquel Iniesta | Dr Nicholas Cummins |
| 22 | AI & future brain tumour treatment: Development of a panel of predictive immunotherapy biomarkers using MRI-based radiogenomic analysis of glioblastomas | Dr Thomas Booth | Dr Marc Modat, Dr Igor Vivanco, Prof Keyoumars Ashkan, Prof Richard Houlston |
| 23 | Face-to-face or remote consultations for people with mental illness in secondary mental health care | Dr Mariana Pinto Da Costa | Prof Fiona Gaughran, Prof Robert Stewart |
| 24 | Bayesian uncertainty quantification and propagation for personalized cardiac modelling | Dr Marina Riabiz | Prof Steven Niederer, Prof Chris Oates |
| 25 | Neurodevelopmental psychiatric conditions: a network analysis approach to the structure of psychopathology | Prof Grainne McAlonan | Professor Federico Turkheimer |
| 26 | Using machine-learning to predict pre-symptomatic intestinal inflammation in Crohn’s disease | Dr Natalie Prescott | Dr Raquel Iniesta |
| 27 | Implementing machine learning methods to integrate radiological and pathological data to assess treatment response in oesophageal/gastro-oesophageal cancer | Dr Anita Grigoriadis | Professor Vicky Goh, Professor Gary Cook, Dr Kasia Owczarczyk |
| 28 | Clinical decision support in management of thoracic malignancy through multi-omic data science | Dr Sophia Tsoka | Professor Vicky Goh, Professor Gary Cook |
| 29 | Vulnerability score for adolescents and young adults attending secondary care | Dr Ingrid Wolfe | Dr Julia Forman |
| 30 | Quality of care for adolescents and young adults | Dr Ingrid Wolfe | Dr Ann Hagell |
| 31 | Comparative health systems performance assessment for children and young people | Dr Ingrid Wolfe | Dr Dheepa Rajan, Dr Ann Hagell |
| 32 | Evaluation of educational and healthcare outcomes and their interaction for children with chronic liver disease in the UK | Dr Marianne Samyn | Dr Katie Harron |
| 33 | A neurosymbolic approach to generating trust in Artificial Intelligence innovations in medicine | Dr Zina Ibrahim | Dr Johnny Downs |
| 34 | Use of artificial intelligence to evaluate effectiveness of polypharmacy in patients with depression-related multimorbidity | Dr Alex Dregan | Dr Jayati Das-Munshi |
| 35 | A fully-quantifiable aetiopathogenic model for Parkinson’s and Overlap and Related Diseases to identify targets for personalized intervention | Prof Steven Gilmour | Dr Sylvia Dobbs, Dr John Dobbs, Dr André Charlett |