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We have 12 Statistics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Birmingham

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Mathematics

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Birmingham  United Kingdom

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Statistics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Birmingham

We have 12 Statistics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Birmingham

A PhD in Statistics focuses on mastering the mathematical framework behind data analysis. You will be researching processes that help understand data and assess risks. Even though Statistics contains a major component of theoretical maths, it has plenty of application in other fields like Physics, Biology or Finance.

What’s it like to do a PhD in Statistics?

On a PhD level, you’ll be using your existing knowledge of the models and methods in Statistics to work on a unique project that offers significant contribution to the field. Statistics is a vast area of study and you can look at one of some of these popular research topics in Statistics:

  • Bayesian statistics
  • High dimensional data
  • Computation techniques
  • Extreme value theory
  • Probability theory
  • Wavelets

Statistics also has applications in other areas like Biology, Medicine, Finance or Physics. You can, therefore, also decide to focus on a particular application of Statistics. For example, you can have a special focus on statistics within biomedical or social science.

Akin to many other STEM subjects, Statistics PhDs are usually advertised with a research objective. You can also propose your own research projects and they might be considered if they meet the over-arching objectives of the department.

In the UK, a PhD will end with submitting a thesis of around 80,000 words followed by an oral examination where you will defend your research in front of an academic panel. It is also likely that you’ll be asked to enroll as an MPhil student at the beginning of your programme. You can upgrade to a PhD, after a review at the end of the first year, if your supervisor feels your work meets certain standards

Since a PhD is a purely research-based degree, there are no compulsory teaching hours. You’ll work on a mutually decided schedule with your supervisor. However, because of the transdisciplinary nature of the field of Statistics, you might be encouraged to take on some taught modules, that cover certain transferable skills, in your first year.

Entry requirements

If you’re looking to do a PhD in Statistics, you’ll need to have completed a Masters (with Merit or Distinction) in a mathematical subject. Some research programmes may also accept degrees in subjects like Physics, Engineering or Computer Science, provided they have a major mathematical component.

Depending on where you choose to apply, you may also need to show that you have a level of language proficiency in your university’s language of instruction

PhD in Statistics funding options

In the UK, the Engineering and Physical Sciences Research Council (EPSRC) funds PhDs in Statistics. They offer fully funded studentships and a monthly stipend to UK students. PhDs are usually advertised with the funding attached and you’re automatically eligible for it if you’re successful in your application.

If you are an international student, you have the option of the EPSRC PhD Scholarships that contribute towards your tuition fee but do not include a monthly stipend.

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PhD in Statistics careers

A PhD prepares you for a career in academia and the industry. Agriculture, forensics, finance and law are some of the biggest employers of Statistics graduates outside of academia. You can also look at careers in Actuarial Sciences after a PhD in Statistics.

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Statistics for Space Weather

Space weather events can negatively effect satellites, the electricity grid, satellite navigation systems and human health. Such consequences have caused space weather to be added to the UK (amongst other nations) National Risk Register. Read more

Machine learning and predictability

There is a growing interest in proposing and implementing different machine and statistical learning methods for predicting modelling outcomes in health, clinical studies and social sciences. Read more

Adjusted-range based self-normalization and its application in time series analysis

Dr. Sun, in collaboration with Professor Yongmiao Hong from the University of Chinese Academy of Sciences and Prof Oliver Linton from the University of Cambridge, is developing an innovative approach to time series analysis using adjusted-range based self-normalization. Read more

Bayesian Nonparametrics for Multiple Systems Estimation

Estimating population size is a common problem in statistics with many well-established methods. However, these methods rely on strict assumptions about the structure of the population. Read more

Data driven approaches for nonlinear inverse problems

The project aims to develop new techniques for solving complex inverse problems that arise in various scientific fields. In many real-world applications, such as medical imaging, geophysics, and material science, we often seek to recover the hidden properties of a system from indirect and noisy measurements. Read more

Neural Networks for Complex Dynamical Systems

Details. Dynamical systems are often solved/integrated by a suitable numerical discretisation method in such a way that certain properties of the underlying systems will be preserved. Read more
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