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We have 92 Applied Statistics PhD Projects, Programmes & Scholarships for Self-funded Students

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Applied Statistics PhD Projects, Programmes & Scholarships for Self-funded Students

We have 92 Applied Statistics PhD Projects, Programmes & Scholarships for Self-funded Students

Identifying critical pathways regulating autoimmunity in immuno-oncology and arthritis patients

Background. Immune checkpoint inhibitors (CPIs) such as BLOCKING antibodies targeting CTLA4 (e.g. ipilimumab) have revolutionised the treatment of metastatic cancers by offering long-term remission, by boosting the immune response. Read more

Development and evaluation of preference-informed, decisional-guidance software for taking drugs to prevent cardiovascular disease

Background and rationale. In choosing whether to take a drug treatment (or other medical intervention), patients weigh up the likely outcomes if they choose treatment against the likely outcomes if they choose no treatment. Read more

Bayesian Computation for Modern Slavery Statistics

Victims of modern slavery are exploited for personal and commercial gain. In 2015, the Home Office estimated that there are 11,000-13,000 victims of modern slavery in the UK (Silverman, 2020). Data about modern slavery is often, noisy, sparse or censored. Read more

Real-time optimisation control of batch processes

Batch processes are widely used in the pharmaceutical, specialty chemical, and food industry for the responsive agile manufacturing of high value added products. Read more

Novel diagnostics to assess the impact of turbulent flow in cardiovascular disease

Cardiovascular disease is the leading cause of death worldwide. Recently, turbulence in blood flow has been linked with cardiovascular disease progression, however the underlying turbulence-related mechanisms are not well understood. Read more

Empirical Modeling in Financial Engineering

This project explores the decvelopment and application of empirical techniques in Financial Engineering. If you are interested, then please complete an application to the PhD in Applied Mathematics. Read more

The automated construction of thermodynamic models for complex mixtures

Process design requires accurate and reliable thermodynamic data for multicomponent mixtures in the system over a wide range conditions (e.g., solvents, impurity concentrations, temperatures, pressures) which are often missing, especially for new compounds. Read more

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