Value of award
A tax-free stipend of £14,777 per year (subject to minor change) and 100% tuition fees at the UK/EU rate plus additional travel allowance.
Number of awards
Start date and duration
As soon as possible for 3.5 years.
The next decade will see step changes in data-driven technology, impacting all aspects of engineering and industry. The emergence of manufacturing protocols for 3D printed stainless steel promises a dramatic increase in the ambition and complexity of structures that can be designed and built. However, these techniques raise urgent statistical challenges which must first be addressed. On the microscale, the inherent variability of advanced printed materials is such that their basic material properties are in effect random, and this variation not yet well-characterised. On the macroscale, how manufacturing standards and safety guarantees can be provided in the context of an uncertain material is yet to be determined. Moreover, it is unclear how inspection and continued monitoring of these structures should be performed.
This project, a joint venture between Newcastle University and the programme on data-centric engineering at the Alan Turing Institute, offers an exciting opportunity to engage with experts in statistical methodology, applied mathematics, probability theory, material testing and engineering from across the UK, so that these urgent questions can be addressed. Detailed experiments are currently being performed to probe the material properties of 3D printed stainless steel. The student will be responsible for the development of novel mathematical models and state-of-the-art computational Bayesian statistical methods for the analysis of these experimental data, to help provide important insight into this new material. A detailed case study will be undertaken on the world’s first 3D-printed bridge, in collaboration with the company MX3D and engineers at Imperial College London.
The student will be based at both Newcastle University and the Alan Turing Institute, London, with the specific arrangement being flexible and to be negotiated – including the provision of additional financial support.
Name of supervisor(s)
Essential: An undergraduate qualification (minimum 2:1 or international equivalent) in mathematics, statistics or engineering with a large component in mathematics is essential. A Masters qualification in a relevant subject area will be highly advantageous. The student must be capable of independent research, as well as possess the skills needed to work successfully in an inter-disciplinary environment.
Desirable: Experience in any of Bayesian statistics, machine learning, numerical analysis and partial differential equations would be useful.
Applicants whose first language is not English require a minimum of IELTS 6.5. International applicants may require an ATAS (Academic Technology Approval Scheme) clearance certificate prior to obtaining their visa and to study on this programme.
How to apply
You must apply through the University’s online postgraduate application system. To do this please ‘Create a new account’.
All relevant fields should be completed, but fields marked with a red asterisk must to be completed. The following information will help us to process your application. You will need to:
insert the programme code 8080F in the programme of study section
select ‘PhD Mathematics (full time) - Statistics, ’ as the programme of study
insert the studentship code MSP008 in the studentship/partnership reference field.