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We have 521 Machine Learning PhD Projects, Programmes & Scholarships

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Machine Learning PhD Projects, Programmes & Scholarships

We have 521 Machine Learning PhD Projects, Programmes & Scholarships

A PhD in Machine Learning focuses on the application of mathematical and statistical models to find patterns in data. You'll be asked to identify, model, and evaluate the systems that can be used to improve the performance of computer systems.

What's it like to do a PhD in Machine Learning?

Traditionally, Machine Learning is divided into different subfields. You'll have the option of choosing from popular fields of study such as Computer Vision and Language Processing.

Some popular Machine Learning subfields are:

Computer Vision

You can opt for a PhD in Computer Vision if you're interested in developing computer systems that can analyse and interpret digital images and video. You'll likely be asked to research methods to improve object classification, image registration, object tracking, and object recognition.

Language Processing

If you're interested in a PhD in Language Processing, you'll be looking at ways to improve the way computers interpret, process, and generate language. You'll be researching methods and techniques to process, store, and search large text collections. You may also be asked to research methods to improve natural language processing.

Machine Learning also has application in other fields such as finance, healthcare, manufacturing, and retail. However, the above are the most popular areas of research.

A PhD in Machine Learning will require you to come up with a research proposal to be defended in an oral exam during your viva. Your research will involve experimentation and observation that may culminate in a publishable paper at the end of your project.

PhD in Machine Learning entry requirements

A PhD in Machine Learning requires a minimum of a 2:1 undergraduate degree in a related subject. You may also be asked to show that you have the necessary pre-entry experience in computer science.

PhD in Machine Learning funding options

A PhD in Machine Learning will most likely have funding attached, meaning if you're awarded a studentship, your tuition fees will be covered and you'll receive a monthly stipend.

PhD in Machine Learning careers

Machine Learning models form the basis of many technologies we interact with every day. A PhD in Machine Learning will equip you with the skills to enter a wide range of careers in industries such as finance, healthcare, retail, and defence. You may also choose to continue your research and aim for a career in academia.

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(EPSRC) Engineering synthetic phages to optimize bacterial killing in phage therapy

Rising antimicrobial resistance threatens global health and risks undermining modern healthcare. A highly promising alternative to traditional antimicrobials is phage therapy, the use of bacteria-specific viruses to treat bacterial infections instead of relying on antibiotics1. Read more

The genetic map of human molecular phenotypes

Rationale. Genome wide associations studies (GWASs) have discovered many genetic associations with a large range of human traits, but the functional consequences of GWAS signals often remain elusive, as most GWAS signals reside in non-coding genomic regions. Read more

Development of A Next-Generation Multimodal Artificial Intelligence Platform for Early Breast Cancer Diagnosis

This Barts Charity funded project will commence in September 2024 and has funding for 4 years. The student will be based at the Barts Cancer Institute, Faculty of Medicine and Dentistry (FMD), Charterhouse Square in the City of London. 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

Causal Machine Learning in Aerospace PhD

This PhD project will investigate the recent field of study of Causal Machine Learning, which aims to modify and augment Machine Learning by using Causal Analysis techniques as a way to solve its limitations. Read more

Simulation-based inference for financial econometrics models

In modern statistical applications, many complicated models have two common features. First the likelihood functions are often difficult to evaluate; second the model is generative. Read more

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