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
- 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.
- 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.
- Fayyaz Minhas (Computer Science) l I work on the development of bespoke machine learning models in computational biology and pathology l see: Eitzinger, Simon, Amina Asif, Kyle E. Watters, Anthony T. Iavarone, Gavin J. Knott, Jennifer A. Doudna, and Fayyaz ul Amir Afsar Minhas. “Machine Learning Predicts New Anti-CRISPR Proteins.” Nucleic Acids Research. April 16, 2020. https://doi.org/10.1093/nar/gkaa219 l working with: Nasir Rajpoot (Computer Science)
- Royle (Medical School) and Brettschneider (Statistics) | Classification of subcellular structures using machine learning 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. In collaboration with Roche and AstraZeneca.
- Andrew Blanks (Medical School) and Magnus Richardson (Mathematics Institute) | In silico reconstruction of myometrial physiology by integrating large data sets of histological micro-architecture with spatially resolved transcriptomics
- Dimitris Grammatopoulos (Medical School), Joanna Collingwood (Dept of Engineering) and Giovanni Montana (WMG) l Biomarker informed Disease modelling and AI-based Prediction tools of severe COVID-19 complications and pregnancy outcomes associated with maternal disease
- David Rand (Maths and Zeeman Institute) and Robert Dallmann (Medical School) | Mutual information in dynamic biological systems