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Details of Grant 

EPSRC Reference: EP/S022945/1
Title: EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath
Principal Investigator: Kyprianou, Professor AE
Other Investigators:
Milewski, Professor PA
Researcher Co-Investigators:
Project Partners:
AstraZeneca UK Limited BT Chinese Academy of Science
CIMAT Cytel Diamond Light Source
DNV GL (UK) Environment Agency (Grouped) GKN Aerospace Services Ltd
IMPA Mango Solutions Moogsoft
National Autonomous University of Mexico National Physical Laboratory Novartis
Office for National Statistics Roche (UK) Royal United Hospital Bath NHS Fdn Trust
Schlumberger Cambridge Research Limited Syngenta University of Amsterdam
University of Mannheim University of Santiago of Chile University of Sao Paolo
Weierstrass Institute for Applied Analys Willis Towers Watson (UK) Wood
Department: Mathematical Sciences
Organisation: University of Bath
Scheme: Centre for Doctoral Training
Starts: 01 October 2019 Ends: 31 March 2028 Value (£): 5,196,269
EPSRC Research Topic Classifications:
Continuum Mechanics Mathematical Analysis
Mathematical Aspects of OR Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Information Technologies Communications
Environment Pharmaceuticals and Biotechnology
Healthcare Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Nov 2018 EPSRC Centres for Doctoral Training Interview Panel E – November 2018 Announced
Summary on Grant Application Form
SAMBa aims to create a generation of interdisciplinary mathematicians at the interface of stochastics, numerical analysis, applied mathematics, data science and statistics, preparing them to work as research leaders in academia and in industry in the expanding world of big models and big data. This research spectrum includes rapidly developing areas of mathematical sciences such as machine learning, uncertainty quantification, compressed sensing, Bayesian networks and stochastic modelling. The research and training engagement also encompasses modern industrially facing mathematics, with a key strength of our CDT being meaningful and long term relationships with industrial, government and other non-academic partners. A substantial proportion of our doctoral research will continue to be developed collaboratively through these partnerships.

The urgency and awareness of the UK's need for deep quantitative analytical talent with expert modelling skills has intensified since SAMBa's inception in 2014. Industry, government bodies and non-academic organisations at the forefront of technological innovation all want to achieve competitive advantage through the analysis of data of all levels of complexity. This need is as much of an issue outside of academia as it is for research and training capacity within academia and is reflected in our doctoral training approach.

The sense of urgency is evidenced in recent government policy (cf. Government Office for Science report "Computational Modelling, Technological Futures, 2018"), through the EPSRC CDT priority areas of Mathematical and Computational Modelling and Statistics for the 21st century as well as through our own experience of growing SAMBa since 2014. We have had extensive collaboration with partners from a wide range of UK industrial sectors (e.g. agri-science, healthcare, advanced materials) and government bodies (e.g. NHS, National Physical Laboratory, Environment Agency and Office for National Statistics) and our portfolio is set to expand.

SAMBa's approach to doctoral training, developed in conjunction with our industrial partners, will create future leaders both in academia and industry and consists of:

- A broad-based first year developing mathematical expertise across the full range of Statistical Applied Mathematics, tailored to each incoming student.

- Deep experience in academic-industrial collaboration through Integrative Think Tanks: bespoke problem-formulation workshops developed by SAMBa.

- Research training in a department which produces world-leading research in Statistical Applied Mathematics.

- Multiple cohort-enhanced training activities that maximise each student's talents and includes mentoring through cross-cohort integration.

- Substantial international opportunities such as academic placements, overseas workshops and participation in jointly delivered ITTs.

- The opportunity for co-supervision of research from industrial and non-maths academic supervisors, including student placements in industry.

This proposal will initially fund over 60 scholarships, with the aim to further increase this number through additional funding from industrial and international partners. Based on the CDT's current track record from its inception in 2014 (creating 25 scholarships to add to an initial investment of 50), our target is to deliver 90 PhD students over the next five years. With 12 new staff positions committed to SAMBa-core areas since 2015, students in the CDT cohort will benefit from almost 60 Bath Mathematical Sciences academics available for lead supervisory roles, as well as over 50 relevant co-supervisors in other departments.

Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Further Information:  
Organisation Website: http://www.bath.ac.uk