Looking to list your PhD opportunities? Log in here.
About the Project
The project concerns numerical solution of partial differential equations (PDEs) with uncertainty in input data. It will focus on developing adaptive algorithms for efficient solution of such problems. This will involve both rigorous mathematical analysis and extensive numerical experimentation. The algorithms will be designed, analysed, and implemented (in a MATLAB environment).
PDEs are key tools in the mathematical modelling of processes in science and engineering. In practical PDE-based models, precise knowledge of inputs (e.g., material properties, initial conditions, external forces) may not be available, or there might be uncertainty about the inputs. In these cases the models are described by PDEs with random data. Such problems arise in many scientific and industrial contexts when it is essential to accurately model complex processes and perform a reliable risk assessment. One of the major challenges in numerical solution of PDEs with random data is the high dimensionality of the resulting discretisations. Therefore, the development of robust and effective numerical methods which make best use of available computational resources is a very active research area.
The project will provide training in modern numerical analysis and uncertainty quantification techniques, thus equipping the student with highly desirable skills for working in either industry or academia.
Entry requirements:
We are looking for an enthusiastic and motivated graduate with
- a 1st class degree in Mathematics, preferably at the MMath/MSc level, or equivalent;
- a solid background in numerical analysis of PDEs;
- good programming skills;
- good communication skills (oral and written).
Good knowledge of probability theory will be beneficial.
Informal inquiries should be directed to Dr Alex Bespalov, e-mail: a.bespalov@bham.ac.uk
Funding Notes
funding may be available through a college or EPSRC scholarship in competition with all other PhD applications;
the scholarship will cover tuition fees, training support, and a stipend at standard rates for 3-3.5 years;
early application is strongly recommended;
the application procedure and deadlines are advertised at View Website;
strong UK/EU candidates are encouraged to make an informal inquiry.
For non-UK/non-EU candidates:
strong self-funded applicants will be considered;
exceptionally strong candidates in this category may be awarded a tuition fee waiver (for up to 3 years) in competition with all other PhD applications.
How good is research at University of Birmingham in Mathematical Sciences?
Research output data provided by the Research Excellence Framework (REF)
Click here to see the results for all UK universitiesEmail Now
Why not add a message here
The information you submit to University of Birmingham will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

Search suggestions
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Birmingham, United Kingdom
Check out our other PhDs in United Kingdom
Start a New search with our database of over 4,000 PhDs

PhD suggestions
Based on your current search criteria we thought you might be interested in these.
Adaptivity and machine learning techniques for PDE problems with uncertain inputs
University of Birmingham
Numerical Algorithms for Molecular Systems and Data Science
University of Birmingham
Computer Vision with Deep Learning for Human Data Modelling
Durham University