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We have 86 Applied Statistics PhD Projects, Programmes & Scholarships

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Applied Statistics PhD Projects, Programmes & Scholarships

We have 86 Applied Statistics PhD Projects, Programmes & Scholarships

AI for Sustainability Policy Appraisal

PhD opportunity to advance AI-driven sustainability policy. Analyse energy usage data to design adaptive incentives and effective policies promoting green energy adoption. Read more

Joint reconstruction-segmentation for imaging

A crucial image processing task is image segmentation. we often seek to identify the presence and location of key features in an image, for example a tumour in a medical scan. Read more

Modelling the ecology and evolution of microbial communities

From the human gut to deep-sea sediments, microbial communities – or microbiomes – colonize virtually every habitat on earth. These microbiomes are central to ecosystem functioning and to host health, and therefore it is essential that we understand how and why they change over time. Read more

Exploring Venus’ atmosphere using data assimilation

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Read more

Modelling of the self-feedback control of the androgen receptor under the influence of testosterone.

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Read more

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

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