Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

We have 91 Applied Statistics PhD Projects, Programmes & Scholarships

Discipline

Discipline

Mathematics

Location

Location

All locations

Institution

Institution

All Institutions

PhD Type

PhD Type

All PhD Types

Funding

Funding

All Funding


Applied Statistics PhD Projects, Programmes & Scholarships

We have 91 Applied Statistics PhD Projects, Programmes & Scholarships

Applied Statistics is the use of statistical methods to solve real-life problems, particularly in fields like health, medicine and social sciences. A PhD in Applied Statistics involves a research project that intents to find solutions to problems identified in different field using the methodologies in the area of Statistics.

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

Using your existing knowledge of Statistics and Maths, you’ll be working on a unique research project that offers significant contribution to the field. As a PhD student in Applied Statistics, you’ll find that what sets it apart from traditional Statistics is the focus on collaboration with other STEM subjects.

Some popular research topics in Applied Statistics include:

  • Linear models
  • Data mining and analytics
  • Statistical process control
  • Spatial statistics
  • Statistical computing
  • Longitudinal analysis s

Your research will probably focus on a particular real-world application of Statistics like in disease mapping, survival analysis or predictive modelling, among others.

You’ll find that most PhD programmes in Applied Sciences are advertised with a research objective already attached. This is the case for most STEM subjects. Even though it is not that common, some universities do consider applicants who want to propose their own research projects provided it meets the overall research objective of the department.

A PhD in Applied Statistics will require you to produce a thesis, around 80,000 words long, to be defended in an oral viva examination.

Entry requirements

A PhD in Applied Statistics will require you to have a Masters with either Merit or Distinction in a subject like Mathematics or Statistics. Some programmes might accept a degree in other fields of study as long as it had a significant mathematical component like Physics or Engineering.

You might also have to prove that you are proficient in the language of instruction at your chosen university.

PhD in Applied Statistics funding options

A PhD in Applied Statistics in the UK is funded by the Engineering and Physical Sciences Research Council (EPSRC) which offers fully-funded studentships and a monthly stipend. PhDs which are advertised with it attached offer guaranteed funding if you are successful in your application. If you are proposing your own project, you’ll first need to be accepted by a university to be eligible for the funding.

PhD in Applied Statistics careers

The skills you’ll acquire while completing a PhD in Applied Statistics will definitely prepare you for a career in academia and research. If you don’t see yourself working as a research fellow or in academia, some of the largest employers of Applied Statistics doctoral graduates are firms in fields like finance, forensics and medicine.

read more

Longitudinal brain imaging and modelling to infer ageing and disease processes

The structure and shape of the brain changes through development, ageing, and in disease. Cross-sectionally, changes in cortical shape and structure correlate with age, cognitive function, and disease severity or progression. Read more

Unravelling the Data Behind Battery Fires

The successful PhD student will be co-supervised and work alongside our external partner FRISSBE- ZAG. This studentship is open to students worldwide *please see funding notes below. Read more

From Waste to Wealth: Dredged Sediments Powering Coastal Renewal

The successful PhD student will be co-supervised and work alongside our external partner JBA. This studentship is open to students worldwide *please see funding notes below. Read more

Utilising statistical principles to improve design and analysis of laboratory experiments

We are looking for an enthusiastic and motivated PhD student to join our team at Brighton and Sussex Medical School. The candidate will work closely with researchers who have extensive expertise in gene expression measurement techniques (such as qPCR and RNA-Seq), data analysis, and statistics.  . Read more

Using machine learning to predict cell-type specific effects of genetic variants which influence genome regulation

PhD Studentship in Machine Learning for Genomics. Imperial College London. White City Campus, London, UK. Applications are invited for a 3.5-year self-funded PhD studentship in the research group of Dr Nathan Skene at the UK Dementia Research Institute, commencing during 2023 for the project. Read more

Agent-Based and Machine Learning Models for understanding the Economic Impacts of AI and/or the Green Transition

Research Focus. Economies worldwide are undergoing transitions driven by new technologies and the move toward a green economy. These transitions, if not done carefully managed, can increase inequality and political polarisation, but under the right policies they can contribute to a more equitable and prosperous future. Read more

Prediction of disease onset by integrated AI approaches

Easier, accurate, and early diagnosis of diseases requires robust biomarkers. Omics approaches that measure proteins, lipids, and metabolites in a biofluid are routinely used to discover phenotypic biomarkers of diseases. Read more

Multimodal stratification of severe mental illness in the Brain & Genomics Hub of the UK Mental Health Platform

Schizophrenia (SZ), bipolar disorder (BD), and schizoaffective disorder (SAD) are the archetypal severe mental illnesses (SMIs) and place an enormous burden on individuals, their carers and wider society. Read more

PhD in Computational Ecology

PhD in Computational Ecology. Why are some ecosystems more resilient to climate change? Can we predict how communities will respond to disturbances? What features of a community ensure stability and coexistence? How do community interactions affect broad-scale biodiversity patterns?. Read more

Filtering Results