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We have 16 Machine Learning PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Reading
Machine Learning PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Reading
We have 16 Machine Learning PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Reading
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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SCENARIO: Machine learning driven balance relationships for next generation data assimilation systems (SC2023_27)
Research Group:
SCENARIO NERC DTP
Are you fascinated by the complex models and systems that are used to produce weather forecasts? Are you a physicist/engineer/mathematician/meteorologist/computer scientist who would like to work towards a PhD with the Met Office in this important area of scientific endeavour?. Read more
Machine learning to predict blood function – towards better haematology analysis
Blood represents a complex mixture of blood cells, plasma proteins, hormones, lipids, salts and water. Blood is good indicator of health, and clinically… Read more
Smart control of crop diseases: how can we best combine fungicides and plant resistance genes?
This project would suit a student interested in combining experimentation in the field with mathematical and computational modelling of agri-food biosystems. Read more
Development of a collaborative robot – human-robot collaboration
Robots work effectively in factories. For example, in car factories, huge robotic arms pick up car parts and join them to the skeleton of the car, working at less than millimetre and millisecond precision. Read more
SCENARIO: Parameterising model bias in data assimilation with application to marine biogeochemistry forecasting (SC2023_28)
Research Group:
SCENARIO NERC DTP
The monitoring and forecasting of the marine biogeochemistry in the shelf seas is essential for understanding the present and future health of our seas and its many associated environmental, economic and societal impacts. Read more
SCENARIO: New approaches to ocean state analysis for climate and forecasting applications (SC2023_43)
Research Group:
SCENARIO NERC DTP
Reconstructing present-day and past ocean temperature salinity and circulation states is critical both to making long-range weather and climate forecasts and for understanding how the ocean has responded over the last century to imbalances in the Earth’s energy budget due to global warming. Read more
SCENARIO: Maximising the value of observational data in ensemble data assimilation for hazardous weather prediction (SC2023_30)
Research Group:
SCENARIO NERC DTP
In a changing climate, an improved ability to forecast hazardous weather is key to the management of risk for society. In weather forecasting systems, large numerical models solve nonlinear equations describing physical processes in the atmosphere. Read more
SCENARIO - Understanding freshwater ecosystem health from a microbial perspective (SC2023_38)
This project is based at the UK Centre for Ecology and Hydrology, Wallingford. Freshwater environments are exposed to a variety of stressors that contribute to the decline of freshwater species, ecosystems, and the services they provide. Read more
Developing computing architectures for improving the simulation of fog
To numerically simulate the formation of fog, a horizontal spatial resolution approaching 1 metre or less is highly desirable to improve scientific understanding… Read more
Local-level governance and environmental protection in Brazil
About the Project. Local authorities are often the ones in charge of taking substantive measures to protect the environment. Read more
Platelets in health, ageing and disease: new diagnostics and treatments
Platelets are small blood cells that play a vital role in the chronic and acute progression of Cardiovascular Disease (CVD), and also have roles in immunity, inflammation, cancer metastasis, Alzheimer's disease and a range of infections, such as dengue, HIV-1, malaria, and COVID-19. Read more
Vehicle Re-Identification Using Self-Supervised Vision Transformers
Vehicle re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search vehicles in a multi-camera network usually having non- overlapping field-of-views. Read more
Zero-Shot Learning for 3D Point Cloud Segmentation
Zero-shot learning is the task of learning new classes that are not seen during training. It has received a lot of attention in recent years particularly in deep neural networks (DNNs) based 2D image classification. Read more
Crop Type Classification Using Optical Remote Sensing
Crop type mapping at the field level is necessary for a variety of applications in agricultural monitoring and food security. In this thesis, the goal is to develop a suitable deep neural network architecture that could detect different crop types in remote sensing images. Read more
Interbrain dynamical functions for anticipating synchronisation under mutual interactions
How can we communicate with other members of society and synchronise our motion in real-time? Crucial to a sense of communication is the ability to entrain perceptually with other members of society, i.e., to be able to follow and to lead, while maintaining individual autonomy. Read more
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