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Details of Grant 

EPSRC Reference: EP/I01358X/1
Title: Warwick Complexity Science Doctoral Training Centre 2
Principal Investigator: Connaughton, Professor C
Other Investigators:
de Lucena, Mrs M
Researcher Co-Investigators:
Project Partners:
Department: Physics
Organisation: University of Warwick
Scheme: Centre for Doctoral Training
Starts: 01 October 2011 Ends: 30 September 2017 Value (£): 3,442,540
EPSRC Research Topic Classifications:
Artificial Intelligence Complexity Science
New & Emerging Comp. Paradigms Non-linear Systems Mathematics
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
18 Nov 2010 DTC Cross Disciplinary Renewals Panel Announced
Summary on Grant Application Form
It is a key challenge for our society to better understand, adapt, design and control complex systems. A complex system comprises many interacting components leading to multiple levels of collective structure and organization. Examples include natural systems ranging from bio-molecules and living cells to human social systems and the ecosphere, as well as sophisticated artificial systems such as the Internet, power grid or any large-scale distributed software system. Ongoing Research Themes:Complexity, Emergence & Upscaling. In mathematically oriented research we attempt to crystallise clear and appliable definitions of information content and emergent behaviour.Complex Fluids and Complex flows. How do a small fraction of interacting particles conspire to dominate their flow properties, and how do those properties influence particular flows? Clustering, Condensation and Jamming. Clustering phenomena are ubiquitous with applications ranging from raindrops to galaxies, and from facebook to traffic jams. Complex Networks & their dynamics. The interplay between the connectivity of a network and its dynamics are central to key challenges today, such as epidemiology, biodiversity, neuroscience and markets. Network Statistical Inference. The inference of network structure is a key approach we use in applications spanning multiple fields, from molecular biology to health and economics. New Applications of Statistical Mechanics. This well developed set of tools finds fresh use in molecular biology, traffic theory and opinion dynamics. Developing Areas include:Complex Systems in Social Science, Epidemiology and related Ecology, and Bioimaging.National Need.The financial crisis shows how urgently the UK needs people trained to understand systemic risk in a complex socio-technical system and to design regulation and incentives to get the economy going again. The debate on climate change shows how vital it is to the UK to have people trained in understanding the implications of policies on a large complex socio-economic-physical-biological system. Our partner BAS (British Antarctic Survey) explicitly recognises Complexity as a divisional aspect of their organisation: Natural Complexity Programme. The debate about management of the National Health Service shows how important it is to involve trained people in designing the incentive and monitoring system. The advances in ability to monitor gene expression provide a huge opportunity for people trained in discerning patterns to contribute to controlling many diseases, particularly cancer. The advances in ability to monitor brain activity create huge opportunities for people trained in understanding network function to contribute to controlling malfunctions such as epilepsy, Parkinson's disease and Alzheimer's disease.
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Organisation Website: http://www.warwick.ac.uk