School of Mathematical Sciences
Mathematic statistic modelling

Mathematics and Statistics for Modelling and Prediction (MaP)

Our Thematic Doctoral Training Programme, MaP, offers a variety of interdisciplinary projects with the theme of Uncertainty Quantification.

About MaP

There are three PhD studentships available, funded by the Engineering and Physical Sciences Council (EPSRC), and some further PhD studentships from other funding sources.

Reseaching at the point of contact between applied mathematics and statistics, we expect successful applicants to focus on modelling real-world problems under uncertainty, working collaboratively with fellow mathematicians, statisticians, researchers from other disciplines and industry contacts.

How to apply

To apply for the programme for 2019 entry please:

  1. Identify three projects of interest
  2. Apply online using the University of Nottingham application page
  3. In the personal statement section indicate that you are applying to the 'Mathematics and Statistics for Modelling and Prediction' programme
  4. Make sure to include a ranked list of your three preferred projects, together with a CV of no more than two pages.

For more information, please contact Professor Andrew Wood.

Eligibility and funding

All candidates should have, or expect to obtain, a First or 2:1 in mathematics, statistics or a related quantitative discipline, such as physics, engineering or computer science.

Fully funded studentships are available for UK applicants. EU applicants who are able to confirm that they have been resident in the UK for at least three years before October 2019 may also be eligible for a full award. EU students who are not able to prove that they meet the residency criteria may apply for a fees only award.

Successful applicants will receive a stipend (£14,777 per annum for 2018/19) for up to three-and-a-half years, tuition fees and a Research Training Support Grant.

Projects

Please identify three projects of interest from this selection. If you have questions about a particular project, please contact the project supervisors directly. 

Bootstrap methods for hypothesis testing in non-linear models
 
Controlling bacterial biofilm formation with shape
 
Developing machine learning and statistical techniques to analyse large ground motion datasets to determine the changes in the state of global peatlands
 
Bayesian inversion in resin transfer moulding
 
Electromagnetic compatibility in complex environments – predicting the propagation of electromagnetic waves using wave-chaos theory
 
Energy storage bed dynamics –the ever-expanding magnesium bed conundrum
 
Form, function and utility in small community energy networks
 
Geometric statistical methods for curves and surfaces with applications in medical imaging
 
Inference for noisy ordinary differential equation models with application to "rust" modelling in sugar beet
 
Linking epidemiological and genomic data for infectious diseases
 
Machine learning for first-principles calculation of physical properties
 
Mathematical Modelling of Lubrication in Grinding Wheels
 
Mathematical Modelling of Powder Snow Avalanches
 
Mechanical modelling of the stability of Earth's peatland carbon reservoirs
 
Modelling flow and crystallisation in polymers
 
Modelling the environmental and genomic interactions in maize under changing climatic conditions
 
Modelling wave propagation in meta-materials: a graph network approach
 
Molecule comparison using electrostatic fields and 3D shape representation
 
Opening the black-box: understanding the mechanisms and behaviours of data-driven water resource models
 
Opening the black-box: understanding the mechanisms and behaviours of data-driven water resource models
 
Quantum tomography for high dimensional systems
 
Stochastic Numerics
 
Text-Analytics for Major Event Detection
 
Thermal characterisation of the building fabric under uncertainty
 

 

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School of Mathematical Sciences

The University of Nottingham
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Nottingham, NG7 2RD

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