These studentships will utilise and develop your expertise in either the mathematical, statistical or computational sciences or biomedical engineering to undertake a preparatory one-year MSc in Interdisciplinary Biomedical Research and an exciting three-year PhD project in applied translational biomedical or healthcare research using novel techniques in Artificial Intelligence and / or Data Science.
The generation of large and complex datasets derived from high-throughput molecular and cellular data (genomics, proteomics and metabolomics), biological and medical imaging to population level data (including epidemiology) is transforming our understanding of the complex environmental, social and molecular determinants of disease. The adoption of new techniques and approaches in artificial intelligence, machine learning and data science will enable greater automation, quantitation and ability to extract important information from these datasets, that will lead both to improved understanding of disease and new approaches for its prevention and therapy. Visit these links for more information:
- Warwick Data Science Institute
- Centre for Artificial Intelligence based in Warwick Manufacturing Group (WMG)
- Institute for Digital Healthcare (WMG) links University of Warwick research to Health Data Research UK
- The Alan Turing Institute: a collaboration between Oxford, Cambridge, Edinburgh, UCL and Warwick
Exemplar PhD projects include..
- Snead (University Hospital Coventry Warwickshire) and Rajpoot (Computer Science) | Machine learning algorithms for improved cancer prognostication.
- Mishima (Medical School) and Hartshorne (University Hospital Coventry Warwickshire) | Automated quality control of in vitro fertilised embryos by machine learning.
- Gibson (Medical School and Chemistry) and Sosso (Chemistry) | Machine learning algorithms to predict the impact of antifreeze agents on intracellular ice formation during cryopreservation.
- Griffiths (Medical School) and Kechagioglou (University Hospital Coventry Warwickshire) | Machine learning from digital communication data and clinical records for improved chemotherapy.
- Hebenstreit (Life Sciences) l Stochastic variation in biology | Next generation sequencing |Single molecule/cell studies |see: Archer N et al, Cell Systems, 2016 l working with: Louise Dyson (Life Sciences, Mathematics); Keith Leppard (Life Sciences); Bruno Frenguelli (Life Sciences); Barbel Finkenstadt (Statistics); Andrew Nelson (Life Sciences).
- Mike Chappell (Engineering) |The modelling and analysis of biomedical, pharmacokinetic and biological processes with specific interest in the emerging field of Quantitative and Systems Pharmacology. l see: Hutchison et al, American Society of Nephrology, 2009.
- Kulkarni (Engineering) l Systems Theoretic Approach and Technologies for Disease Diagnosis and Therapy Interventions l working with several medical doctors in and around Coventry and with biotech research firms such as Agilent, Medtronic, Pfizer, L’Oreal, Microsoft Research, and others.
- Royle (Medical School) and Brettschneider (Statistics) | Classification of subcellular structures using machine learning. l Diagnosing the subcellular location of each of the 20,000 proteins expressed in cells is a major problem in biomedical research. The PhD student will use machine learning methods to classify images of proteins with known localisations to be able to predict, from similar cellular images, the location and possible function of uncharacterised proteins in human cells.
- Deepak Parashar (Medical School) l Biomarker Data Analytics for Stratified Medicine in Cancer.
- Andrew Blanks (Medical School) and Hugo van den Berg (Mathematics Institute) | In silico reconstruction of myometrial physiology by integrating large data sets of histological micro-architecture with spatially resolved transcriptomics
- Matt Keeling (Zeeman Institute) and Moran (Heart of England NHS Trust) | Using electronic data of health-facility and community transmission to guide empirical antibiotic choice
- Dimitris Grammatopoulos (Medical School) and Wernisch (MRC Biostatistics Unit, Cambridge Institute of Public Health) | Prediction modelling of perinatal depression risk applying dense psychosocial, genetic and biochemical marker data
- David Rand (Maths and Zeeman Institute) and Robert Dallmann (Medical School) | Mutual information in dynamic biological systems
- Karuna Sampath (Medical School) and Till Bretschneider (Computer Science) | Massively parallel imaging to understand morphogen dynamics during development and disease.
- Sosso (Chemistry) l I am a computational scientist, chiefly interested in the physical chemistry of condensed matter, from supercooled liquids to biological interfaces. My vision is to understand the chemistry, the functional properties and the phase transitions of different classes of systems, from metallic glasses to cellular membranes. My approach consists in performing fundamental research using computer simulations, aimed at rationalise, complement and guide experiments and applications. l see Sosso et al, Chemical Science, 2018 l working with Matt Gibson (Medical School and Chemistry), Andy Marsh (Chemistry), Ann Dixon (Chemistry), James Kermode (Engineering).
- Anne Straube (Medical School) and Grosskinsky (Mathematics Institute) | Understanding the cooperation between molecular motors in cargo transport through mathematical modelling.