Development of an Intelligent Decision Support System by Machine Learning for Ultraprecision Machined Functional Surfaces and their Metrology Assessment
We are recruiting new Doctoral Researchers to our EPSRC funded Doctoral Training Partnership (DTP) PhD studentships starting 1 October 2024. Applications are invited for the project Development of an Intelligent Decision Support System by Machine Learning for Ultraprecision Machined Functional Surfaces and their Metrology Assessment
Successful applicants will receive an annual stipend (bursary) of £21,237, including inner London weighting, plus payment of their full-time home tuition fees for a period of 42 months (3.5 years).
You should be eligible for home (UK) tuition fees there are a very limited number (no more than three) of studentships available to overseas applicants, including EU nationals, who meet the academic entry criteria including English Language proficiency.
You will join the internationally recognised researchers in the Department of Mechanical Engineering research and PhD programmes | Brunel University London
The Project
Ultraprecision machining along with surface measurement systems, such as optical 3D surface profilers, are used for an ever-increasing number of applications in different industries, including automotive, aerospace, optics, semiconductor and medical, to produce high precision functional surfaces of an extensive range of materials, from metals to glasses and other transparent materials. However, considerable effort and experience level are typically required to find the optimal set of parameters for each and every application, and skilled human intervention is usually required to obtain consistent surfaces that satisfy their functional requirements and service life of the manufactured product. The aim of this project therefore is to reduce this reliance by developing an intelligent decision support system for ultraprecision machining of functional surfaces using emerging machine learning algorithms with Bayesian treatment due to the uncertain nonlinear relationship between ultraprecision machining and surface measurement functions, and the extremely tight tolerances involved.
Please contact Dr Moschos Papananias at moschos.papananias@brunel.ac.uk or Prof. Kai Cheng at kai.cheng@brunel.ac.uk for an informal discussion about the studentships.
Eligibility
Applicants will have or be expected to receive a first or upper-second class honours degree in an Engineering, Computer Science, Design, Mathematics, Physics or a similar discipline. A Postgraduate Masters degree is not required but may be an advantage.
Skills and Experience
Applicants will be required to demonstrate the following skills:
- Programming software/languages, such as Python, MATLAB and C/C++
- Data analytics, machine learning and Bayesian inference
- Design modelling and analysis of high precision surfaces
- AI techniques applied to handling image data of optical surfaces metrology
You should be highly motivated, able to work independently as well as in a team, collaborate with others and have good communication skills.
How to apply
There are two stages of the application:
1.Applicants must submit the pre-application form via the following link
https://brunel.onlinesurveys.ac.uk/epsrc-dtp-24-25-pre-application-form-brunel-university-lon
by 16.00 on Friday 5th April 2024.
2.If you are shortlisted for the interview, you will be asked to email the following documentation in a single PDF file to cedps-studentships@brunel.ac.uk within 72hrs.
- Your up-to-date CV;
- Your Undergraduate degree certificate(s) and transcript(s) essential;
- Your Postgraduate Masters degree certificate(s) and transcript(s) if applicable;
- Your valid English Language qualification of IELTS 6.5 overall (minimum 6.0 in each section) or equivalent, if applicable;
- Contact details for TWO referees, one of which can be an academic member of staff in the College.
Applicants should therefore ensure that they have all of this information in case they are shortlisted.
Interviews will take place in April/May 2024.