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Data Intensive Research and Scientific Statistical Modelling

Data Intensive Research

Over the last decade or so vast amounts of data are routinely collected in typical experiments and needs to be analysed. New inferential techniques are now needed to intelligently sort and synthesise these vast information sources so that sound and focussed inferences can be made. Perhaps two of the most common domains facing these challenges lie in the science of biological regulation and in brain imaging - areas of intense research at Warwick - although the problems also extend to both governmental and industrial domains as well.

Warwick statisticians boast international acclaim for their contibution to the development of new inferential methods to address these issues. In particular the department has several members who are fellows of the AlanTuring Institute which provides a hub of five top universities at the very centre of this research within the UK.

Staff members working in this area include David Wild, Thomas Nichols, Theo Damoulas, Jane Hutton, Adam Johansen.

Potential PhD projects in Scientific Data Intensive Research can be found here.

Scientific Statistical Modelling

Galileo Galilei told us that mathematics is the language of science, and David Hilbert described mathematics as an instrument that mediates between thought and observation. In our days, observations have become complex and rich sets of data, making probabilistic modelling and statistical methodology key technologies to build models for scientific discoveries.

In collaboration with scientists you can model components of scientific experiments, identify sources of variation, model quantify risks, predict behaviour and make decisions under uncertainty. The objectives can be to tackle basic scientific questions, such as understanding of role of genes for a certain biological process or disease, understanding spatial patterns in archeological findings or the modelling of noise in computed tomography. They may aim at concrete applications such as quality control of novel genomic technologies, detection or flaws in 3D-printed objects or individualisation of medical treatments based on measurements of the patient’s circadian clock.

Warwick statisticians are involved in numerous collaborations with natural scientists, social scientists and clinicians on campus and in other universities around the globe and maintain relationships with a variety of industrial partners.

Staff members working in this area include Julia Brettschneider, Wilfrid S. Kendall, David Wild, David Firth, Simon Spencer, Baerbel Finkenstaedt, Rich Savage, Anastasia Papavasiliou, Tom Nichols, Jane Hutton.

Potential PhD projects in Scientific Statistical Modelling can be found here.

A typical student's experience -- Nat Shiers

Having completed an integrated masters in Mathematics and Statistics at Warwick, I was already aware that the department offered an excellent research environment. The high level of expertise of academics and the quality of the supervision made the transition from a taught to a research degree very smooth. The atmosphere of the department is very friendly and everybody is really approachable, which I found to be conducive to an enjoyable research experience.

The department provides great facilities for PhD students plus many opportunities. In the first year I attended the Academy for PhD Training in Statistics which broadened my understanding of statistics at a high level and was also a fantastic chance to meet Statistics PhD students from other universities. During my time in the department I was also able to present work at the Research Students Conference and at international conferences. Within the department there are regular seminars including CRiSM and Royal Statistical Society talks, plus weekly Young Researchers Meetings where PhD students can present their work in a more informal setting. One of the my favourite experiences was the annual visit to Gregynog Hall, a lovely setting for a long weekend statistical retreat.