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We have 12 Machine Learning PhD Projects, Programmes & Scholarships in Newcastle
Machine Learning PhD Projects, Programmes & Scholarships in Newcastle
We have 12 Machine Learning PhD Projects, Programmes & Scholarships in Newcastle
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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PhD Studentship in Machine Learning and Molecular Dynamics
Overview. Interested in machine learning (ML) for molecular modelling? This PhD project aims to develop machine learning inter-atomic potentials (MLIPs) for molecular materials and organic-inorganic interfaces relevant to energy storage technology and medicinal applications. Read more
Next-Generation Spiking Neural Networks Architectures and Machine Learning Algorithms
Spiking Neural Networks (SNNs) represent the so called ‘third generation’ of artificial neural network models, that bridge the gap between neuroscience and artificial intelligence by relying on biologically realistic models of neurons and network architectures to carry out computations. Read more
Deep Learning and Explainable Artificial Intelligence for Flood Prediction (FAC23/EE/CIS/WOO)
Flood prediction models are crucial for managing extreme events and assessing hazards. However, current models often make simplified assumptions and rely on specific datasets. Read more
PhD Studentship in Computer Science: Internet of Things
Overview. We live in a connected world with a range of devices, such as smart appliances, smartphones, wearables, smart cars, and others that compose the Internet of Things (IoT). Read more
PhD Studentship in Computer Science: Computational Neurology (Epilepsy)
Overview. We are seeking a highly motivated PhD student to develop computer models of the brain in order to optimise epilepsy treatments such as surgery and stimulation, and develop new treatments such as chronotherapy. 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
Digital twining for deformable objects – achieving real-time simulations
A step change in the simulation of the complex interactions between flexible objects and immersing fluid flows is required in the development of digital… Read more
An optimal framework for disease risk prediction and stratification for multi-ethnic populations (ref: SF22/HLS/APP/Chimusa)
The exceptional polygenicity of human traits makes unravelling mechanisms from disease risk prediction models daunting. The development of polygenic risk scores (PRS) methods, their evaluation and clinical utility have been explored almost in European ancestry Genome-Wide Association Studies (GWAS) data sets. Read more
Estimation of food portion sizes through smartphone images (ref: SF22/HLS/APP/BROWNLEE)
Accurate and precise assessment of dietary intake is crucial to consideration of population-to individual-level patterns of food consumption with clear links to consequent impacts on long-term health and national/global food requirements. Read more
Transforming neuro-pathology with deep learning (ref: SF22/HLS/APP/Schwalbe)
Medulloblastoma is the most common central nervous system tumour of childhood. Although advances in treatment have raised survival to ~75%, there remains significant numbers of patients who die of their disease. Read more
Quantifying the impacts and consequences of future climate, super-events
There is a great deal of anecdotal evidence that some storms are particularly damaging. For example, for Storm Arwen it has been said that it was… Read more
Autonomous reality capture: from 3D data acquisition to digital twin creation
With the recent release of Hexagon Geosystem’s BLK ARC and BLK2FLY technologies, the focus of creating automated large-scale 3D geospatial digital twins evolves from that of efficient primary data acquisition entirely to endgame interpretation and creation of the Building Information Model (BIM). Read more
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