Applications open: Monday 30th April
Applications close: Monday 28th May, 23:59
King’s College London are seeking 3 outstanding and motivated students to join our health faculties. Human health is a dominant and unifying research theme at King’s College London. We are internationally recognised for excellence in our training programmes in biomedical and health research.
MRC DTP PhD Studentships in Data Science & Artificial Intelligence
Three 3.5 year fully-funded PhDs are available to commence in October 2018. The successful candidates will pursue a 3.5 year PhD and will be part of the MRC Doctoral Training Partnership. They will start immediately on their PhD research project in October 2018 and will further benefit from the core and specialised research training, personal mentorship and cohort activities that are provided through the MRC DTP.
All projects are available as a straight 3.5 PhD. Applicants must apply to one project only. You are advised to contact project supervisors for further information about the projects.
Co-supervisor 1A: Dr Anita Grigoriadis
Co-supervisor 1B: Dr Saeed Shoaie
In cancer, patient selection is central to the success of targeted therapy and characterising the tumour-specific molecular landscape is essential to guide treatment choices. Each tumour in each patient has a unique and heterogeneous genomic landscape. Next generation sequencing data has revealed that the genomic landscape of each tumour is constantly changing over time as a result of clonal evolution imposed on cancer cells by the surrounding microenvironment, the patient’s immune system and the selective pressure of therapy. Recently, we also became aware of the human microbiome, the complex ecosystem of thousands of different microorganisms that call our bodies home, to influence tumour growth and overall disease progression.
Longitudinal surveillance of clonal cancer evolution is pivotal for patient’s individual medicine. While sampling tumour tissue is very difficult to achieve effectively, liquid biopsies including blood, urine, stool and oral rinses of cancer patients is ideal to measure diverse tumour-derived substances, such as cell-free tumour DNA (ctDNA), and microbiota. While sampling and molecularly analysing ctDNAs in patients’ blood allows a minimally invasive monitoring of tumour, offers a comprehensive picture of the spread or remission of a cancer, the microbiome can be used as a readout of the systemic responses of a patient, and clinicians can adapt treatments accordingly.
The ultimate aim is to identify robust markers in the ctDNAs and the microbiome from cancer patients which can reflect patient’s responses to different treatment strategies. The student will collate diverse molecular data from gene expression and copy number levels from the cancer, in parallel to diverse microbiotas, and treatment response data of cancer patients, as well as drug-sensitivity data from in vitro models. By implementing and comparing Bayesian statistical methods with deep learning techniques, the pros and cons of these analytical avenues will be tested and inform their application and molecular marker selection in ctDNA and microbiota compositions to predict treatment response.
Brasó Maristany F, Filosto S, Catchpole S, Marlow R, Quist J, Domenech EF, Plumb DA, Zakka L, Gazinska P, Liccardi G, Meier P, Serra V, Gris S, Cheang MC, Perdrix Rosell AP, Shafat M, Noël E, Patel N, McEachern K, Scaltrii M, Noor F, Buus R, Mathew S, Watkins J, Marra P, Grigoriadis A, Tutt AN.
PIM1 kinase regulates cell death, tumour growth and chemotherapy resistance revealing a novel target in triplenegative breast cancer. Pages 1303-1313, Nature Medicine, Volume 22, Issue 11, 1 November 2016. ISSN: 10788956
Co-supervisor 1A: Professor Cathryn Lewis
Co-supervisor 1B: Dr Paul O’Reilly
Niels Bohr stated that ‘It is Difficult to Make Predictions, Especially About the Future’ but the development of data science methods allows us to build increasingly effective predictive models in large data sets. This PhD project will apply machine learning and deep learning methods, as well as classic statistical models, to the UK Biobank, an incredible health study of over 500,000 people in the UK. The student will integrate genetic, environmental and clinical data to predict onset of diseases that are relevant for the UK’s aging population such as heart disease and cancer. A particular focus will be assessing the utility of genetic information: does genetics add information to routinely-collected clinical and biomarker data, and what role could genetics play in clinical prediction algorithms?
In Year 1, the student will develop their programming, analytical and ‘big data’ skills, building classic statistical models and machine learning algorithms to assess the predictive ability of clinical data (including biometrics and blood biomarkers), lifestyle data (such as smoking habits, diet and exercise) and genetic predisposition in coronary artery disease. In Year 2, novel genetic risk scores will be built for different disorders, using machine learning methods, and their predictive ability assessed, in combination with all other sources of information. In addition, machine/deep learning methods will be used to identify new environmental risk factors. In Year 3, the student will build comprehensive disease risk models and test their predictive power against the gold-standard clinical prediction tools.
Euesden J, Lewis CM, O’Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015 May 1;31(9):1466- 8. doi: 10.1093/bioinformatics/btu848.. PMID: 25550326
Krapohl E, Patel H, Newhouse S, Curtis CJ, von Stumm S, Dale PS, Zabaneh D, Breen G, O’Reilly PF, Plomin R. Multi-polygenic score approach to trait prediction. Mol Psychiatry. 2017 Aug 8. doi: 10.1038/mp.2017.163. PMID: 28785111
Co-supervisor 1A: Dr Sophia Karagiannis
Co-supervisor 1B: Dr Sophia Tsoka
Complex networks can be used to describe a wide variety of systems of high technological and intellectual importance, such as biomedical or disease-related networks. Data science and network analysis aim to model individual pairwise interactions between genes and their products in a holistic manner, so that the properties of the entire system dynamics, such as self-organisation or adaptiveness, can be revealed.
In the study of biological systems, community structure detection, as part of Data and Network Science, provides a topological perspective of cellular interactions at system-level and can lead to critical insights into the functional organisation of the underlying molecular processes. Here, we propose application of complex systems approaches in the study of cancer therapy signalling. We propose a tight interaction between computational and experimental scientists to establishing accurate models of molecular interactions in cancerous cells engendered by monoclonal antibodies to a tumour antigen. In particular, we will investigate mechanisms where antibodies induce downstream signals to destroy cancer cells. We will apply our methodologies to networks constructed from experimental data, including gene expression and phosphorylation data, to represent and compare system behaviour under various conditions.
This work will contribute towards understanding the combination of pathways and other molecular interactions associated with antibody-mediated cancer clearance. Overall, our proposal has a strong interdisciplinary and translational outlook in establishing the use of Data Science in an emerging biomedical system, such as the use of antibody biologicals for cancer therapy.
Josephs DH, Bax HJ, Dodev T, Georgouli M, Nakamura M, Pellizzari G, Saul L, Karagiannis P, Cheung A, Herraiz C, Ilieva KM, Correa I, Fittall M, Crescioli S, Gazinska P, Woodman N, Mele S, Chiaruttini G, Gilbert AE, Koers A, Bracher M, Selkirk C, Lentfer H, Barton C, Lever E, Muirhead G, Tsoka S, Canevari S, Figini M, Montes A, Downes N, Dombrowicz D, Corrigan CJ, Beavil AJ, Nestle FO, Jones PS, Gould HJ, Sanz-Moreno V, Blower PJ, Spicer JF, Karagiannis SN. Anti-Folate Receptor-α IgE but not IgG Recruits Macrophages to Attack Tumors via TNFα/MCP-1 Signaling. Cancer Res. 77(5): 1127-1141, 2017.
L. Bennett, A. Kittas, G. Muirhead, L. G. Papageorgiou, S. Tsoka, “Detection of Composite Communities in Multiplex Biological Networks”, Scientific Reports, 5:10345, 2015.
AWARD TYPES & ELIGIBILITY
Stipend: Students will receive a tax-free stipend for each year of study, this rate will be in line with the MRC’s minimum rates. For 2018/2019 this will be £16,777 per annum.
Bench fees: A generous allowance (£5,300) will be provided for research consumables and for attending UK and international conferences.
Eligibility: The MRC funding available supports Home/EU students within standard research council restrictions. EU students are only eligible for a full studentship if they have lived, worked or studied within the UK for three years prior to the funding commencing. More information can be found on the MRC website.
Unfortunately, we cannot accept applications from non-EU candidates.
The programme is very competitive and applicants must have or be predicted to obtain a high quality B.Sc. degree with a 1st or a high 2.1, or a master’s qualification at Merit/Distinction grade.
If English is not your first language you will be required to provide evidence that you meet the minimum English requirements of the Faculty [Band D] as prescribed by the University’s English Language requirements: http://www.kcl.ac.uk/study/postgraduate/apply/entry-requirements/english-language.aspx
If you are unable to provide this confirmation before applying any offer you are made would be conditional upon you meeting these requirements prior to enrolment and no later than the 31st of August 2018.
You can contact the King’s Admissions team for further information at: http://www.kcl.ac.uk/study/postgraduate/apply/contact-us.aspx
- Under Programme Name type ‘MRC DTP studentships in Data Science & Artificial Intelligence’
- The MRC DTP PhD Studentships will be listed
Once the programme has been selected, please select the start date (only one date is available) and read the on-screen information about how to progress through the application. Please pay careful attention to the on-screen information; it is the applicant’s responsibility to submit all required documents.
Please be sure to complete the following sections of the form with all relevant information (all questions with an asterisk are mandatory and you will not be able to submit without answering these):
- Personal Information*
- Employment history* (you will be able to enter up to five sets of employment information)
- CV*: upload a PDF copy of your CV as an attachment to the Employment History Section.
- The ‘Nature of work’ field allows only 50 characters, but you can upload further employment information on the references screen
- Personal statement*. Please include your personal statement in this section. You should type your personal statement directly into the Personal Statement free text box (Personal Statements uploaded onto the application as a separate PDF will not be accepted). There is a 4000 character limit, so please ensure that any text you plan to copy and paste into the box is within these limits (you should note that the Personal Statement character limit includes punctuation and spaces and so may differ from the character count in your source text).
- Fee Status Questionnaire: If you are an EU Applicant, please attach a complete MRC DTP Fee Status Questionnaire 2018 Entry to the Personal Statement section, this is a mandatory requirement of all EU Applicants.
- References*: Contact details for two academic referees or relevant employers in research institutions/companies (we will then contact your referees directly).
- Funding*: In the funding section of the online application form please enter the funding code ‘MRCDTP2018_′ followed by the project number that corresponds to your chosen project e.g MRCDTP2018_1.6 (see drop down menu below for all funding codes).
- Research Proposal*:
‘Project Title’: title of project
‘Project Proposal’ free-text box: Please write the project number followed by the full title e.g 1.6:’’Project Title’’
- Finally, check and submit your application.
Additional Application Support
Your personal statement should include the following elements:
- Why do you want to join this PhD programme? Try to convey your enthusiasm and motivation for research. Do you understand the demands of postgraduate research?
- Why have you chosen this project?
- Why King’s College London?
- What is the relevance of your first degree to this study? Comment on relevance of courses you have taken at university. Point out any circumstances that may have effected your academic results, that you think should be considered.
- What academic skills have you got to offer? Knowledge of relevant scientific topics and techniques. Experiences of research projects you have done. Academic prizes you have been awarded.
- What personal skills can you offer?
Demonstrate that you have considered your strengths and weaknesses for postgraduate research. Can you demonstrate the dedication and resilience required to complete a PhD?
- What are your strengths?
In what ways are you better than other applicants?
- What are your career aims? Tell us your short-term aims and long-term career ambition
Your references must contain contact details for two academic referees or relevant employers in research institutions/companies (we will then contact your referees directly). Note that academic referees must have university email addresses and employer references should have the official email address of the company (gmail, hotmail etc addresses are not acceptable). If you already have two academic references, you can scan and upload these to the online application instead (note that they must be signed and on headed paper).
Please remember that it is your responsibility to ensure we have received the references by application deadline; ensure to start your application before the deadline and contact your referees to let them know we will be requesting a reference from them.
The MRC funding available supports Home/EU students within standard research council restrictions. EU students are only eligible for a full studentship if they have lived, worked or studied within the UK for three years prior to the funding commencing. To ensure that EU applicants meet this eligibility requirement, all EU applicants must complete and attach a MRC DTP Fee Status Questionnaire to the Personal Statement of the online application form.
In the funding section of the online application form please enter one of the following codes that corresponds to the Project you are applying for:
- MRC DTP2018_3.6
- For example if the project you are applying to is project 1, the code that you should enter is ‘MRCDTP2018_1.6’
Project Title: Type title of project chosen
State your project choice (Project number followed by project title) in the free-text box under the Research Proposal section of the application, for example:
1.6 ‘’Project Title’’
Q. When will I know I have been shortlisted?
All shortlisted applicants will be informed no later than Friday 18th June. If you have not received an email invitation by this date, unfortunately you have not been successful this time. Please take a look at other Health School Studentships available at King’s.
Q. Where and when will the interviews take place?
The interviews will take place on Wednesday 20th June on Guy’s Campus.
Q: Do I need a master’s degree to apply?
No. The programme is very competitive and applicants must have, or be predicted to obtain, a high quality BSc degree with a 1st or a high 2:1. Alternatively, students must have a master’s qualification at Merit/Distinction grade.
Q. Where can I find a list of the projects available?
Projects will be available when the studentships are advertised. Please refer to the top of this webpage under ‘Projects’ to view the projects available for funding.
Q. How many referees do I need?
On your application, you must provide the contact details of two academic referees. If you feel it would be more appropriate, we can accept one reference from a non-academic research environment, e.g.: an employer from a research institution or company.
Note: academic references must list a university email address and employer referees should list the official email address of the company. If you already have two references you can scan and upload these instead (note that they must be signed and on headed paper).
Note: references must be received by 23:59 on Monday 28th May, 2018. Be sure to start your application before the deadline and contact your referees to let them know we will be requesting a reference from them. It is your responsibility as the applicant to ensure the references are submitted by the deadline.
Q. When is the deadline for applications?
We will be accepting applications up until 23.59 on Monday 28th May, 2018. Applications submitted after this date will not be considered. We advise that you do not wait to submit your application until close to the deadline, in case there are problems with uploading documents etc.