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We have 107 Computational Mathematics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships
Computational Mathematics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships
We have 107 Computational Mathematics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships
A PhD in Computational Mathematics involves the use of complex mathematical models to find solutions to complex problems in fields such as Physics and Engineering.
What's it like to study a PhD in Computational Mathematics?
As a PhD student in Computational Mathematics, you'll work with your supervisory team to come up with solutions to real-world problems using mathematical models. You'll likely divide your time between lab-based research and writing your thesis.
Some popular research areas in Computational Mathematics include:
- Computational imaging
- Computational mechanics
- Computational finance
- Data science
- Mathematical biology
- Statistical physics
You might also have the opportunity to collaborate with other departments at your university to connect your research to their work.
Most PhDs in Computational Mathematics are advertised with a research objective attached, but it's also possible to propose your own research project. Your proposal will need to be reviewed and accepted before you can begin your PhD.
In the UK, you'll usually complete a PhD in Computational Mathematics over three years, although some departments may ask you to study over four years.
If you are performing research that is connected to the work of other departments at your university, you may be asked to divide your time between working on your own research and assisting with other projects.
Entry requirements
Because it is a research-led degree, entry requirements for a PhD in Computational Mathematics are the same as those for a Masters in Computational Mathematics. You'll most likely need an Upper Second-class Honours degree in Mathematics or a relevant subject, such as Physics, Engineering or Computer Science.
You may also need some professional experience in Computational Mathematics, depending on the programme you apply to.
PhD in Computational Mathematics funding options
In the UK, PhDs in Computational Mathematics are funded by the Engineering and Physical Sciences Research Council (EPSRC). They offer full-time students a fully-funded studentship, along with a tuition fee waiver and a living cost stipend.
If you're applying for a PhD advertised with a research objective attached, you'll receive any funding attached to the project. If you're proposing your own project, you'll first need to be accepted onto a PhD to be eligible for funding.
PhD in Computational Mathematics careers
A PhD in Computational Mathematics can open many doors to careers in scientific research and computing, as well as finance and technology. You may also have the opportunity to continue your research career by applying for a postdoc or research fellow position.
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Robust Updating and Digital Twin Extrapolation in Space Object Re-entry Monitoring
The Strathclyde Centre for Doctoral Training (SCDT) in "Data-driven uncertainty-aware multiphysics simulations" (StrathDRUMS) is a new, multi-disciplinary centre of the University of Strathclyde, which will carry out cutting-edge research in data-driven modelling and uncertainty quantification for multiphysics engineering systems. Read more
Industry-funded PhD Studentship with Exscientia in Structural Bioinformatics
Many viruses, including major human pathogens, encode virus assembly instructions in their genomes. This “assembly code” consists of multiple dispersed sequence/structure motifs called packaging signals, that play key roles in efficient particle formation. Read more
Generative Deep Learning Models for Automated Craniofacial Surgical Planning
Aims of the Project. Implement a deep learning-based approach to detect and localise craniofacial anomalies in children. Develop a generative method to simulate a stereotypical paediatric skull from a skull with detected anomalies using a skull atlas as reference. Read more
Physically-informed learning-based beamforming for multi-transducer ultrasound imaging
Aim of the PhD Project. Pursue sparse solutions to handle the channel count required to coherently operate multiple ultrasound transducer and design and implement machine learning strategies to avoid the sparsity-related artefacts in the images. Read more
Respiratory Viruses in Care Homes: Development of Transmission Models
The objective of the 2 PhD studentships are to develop simulation-based models to explore the transmission of respiratory viral pathogens in vulnerable settings. Read more
Personalised surveillance schedules for detecting hepatocellular carcinoma
Early diagnosis of cancer allows early intervention which is expected to improve outcomes for patients. Surveillance is a tool that can aid early detection through regular assessment of patients. Read more
Simulating the uncertain future of the Antarctica Ice Sheet and the role of ice-ocean feedbacks (FAC22/EE/GES/DERYDT)
The Antarctic Ice Sheet is losing ice at an increasing rate, making it one of the largest contributors to present-day sea level rise. Read more
Wavelet adaptive multiresolution representation for turbulent reacting flows
Supervisory Team. Temistocle Grenga, Ed Richardson. Project description. Hydrogen will be, among the synthetic fuels, the preferable energy carrier able to address the spatial and temporal separation between energy production (renewable source) and consumption. Read more
Assessing the extinction risk and recovery potential of species with Deep Learning (WUT_U23CMP)
Deep learning, a powerful class of artificial intelligence (AI) algorithms, is emerging as a promising computational framework for inferring evolutionary signals from highly complicated population datasets [1]. Read more
System dynamics modelling and simulation of sufficiency policies in complex supply chains
Short Description. This PhD project aims to model and simulate the potential rebound effects of sufficiency policies in complex production and consumption systems using a system dynamics approach. Read more
Modelling the Large-Scale Structure of Turbulence in Jets
We are pleased to present an exciting new PhD opportunity in Computational & Theoretical Aero-acoustics. The position is based at the Department of Mathematics at the University of York. Read more
Using Scientific Machine Learning to Investigate the Electromechanical and Energetic Changes of Genetic Mutation-induced Hypertrophic Cardiomyopathies: An Embodied AI Approach
The project involves the use of reinforcement learning, optimal control and computer vision techniques to understand the causal link between genotype and phenotype in Hypertrophic Cardiomyopathy. Read more
Investigating the role of verified methods for enhancing trust in digital twins
The Strathclyde Centre for Doctoral Training (SCDT) in "Data-driven uncertainty-aware multiphysics simulations" (StrathDRUMS) is a new, multi-disciplinary centre of the University of Strathclyde, which will carry out cutting-edge research in data-driven modelling and uncertainty quantification for multiphysics engineering systems. Read more
Transparent AI and differential equations
Machine learning and its applications have grown explosively in the last few years. However, the success of machine learning is still poorly understood from a theoretical point of view. Read more
Transparency in neural ordinary differential equations
Neural ordinary differential equations (neural ODEs) are a relatively recent development (. https://arxiv.org/abs/1806.07366). in the field of machine learning, where the hidden state dynamics of certain neural network architectures can be reformulated as numerical solutions of a differential equation. Read more