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Physics-Informed Data-Driven Modelling of Aluminium Processing

Applications are invited for a PhD studentship for the project “Physics-Informed Data-Driven Modelling of Aluminium Processing, effective 1st February 2022. Successful applicants will receive an annual stipend (bursary) of £17,609 plus payment of their full-time tuition fees for a period of 36 months (3 years).

The successful applicants will join the internationally recognised researchers in BCAST at Brunel University London (BUL). This exciting research project is focused on the development and application of physics-informed machine learning to predict engineering properties of aluminium alloys in various stages of the manufacturing process. The research will include: (1) application of artificial neural networks to analyse the existing experimental data on aluminium processing, (2) numerical simulation of the manufacturing process to generate additional information to train the neural network, and (3) exploring possibilities to design specialised network architectures that are consistent with the relevant physical laws. The project will support activities of Materials Made Smarter Centre (MMSC), which will deliver multidisciplinary research in the area of Digital Manufacturing. The student will have the opportunity to interact with and receive advice from the MMSC’s leading researchers in materials, physics-based modelling, and data science, across the centre’s five partner institutions (Universities of Sheffield, UCL, Cambridge, Brunel, Nottingham, and Swansea).

Please contact Professor Hamid Assadi at hamid.assadi@brunel.ac.uk for an informal discussion about the project.


Open to Home Students, EU Students, and International Students

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.

Applicants will be required to demonstrate their abilities in numerical modelling of materials processing or in the application machine learning techniques in scientific / engineering problems.

Experience in artificial neural networks and computer programming is an advantage. In addition, applicants should be highly motivated, able to work independently as well as in a team, collaborate with others and have effective communication skills.

How to apply

Please submit your application documents (see list below) by Noon on 17 December 2021 to

cedps-pgr-office@brunel.ac.uk   Interviews will take place in early in January 2022.

  • Your up-to-date CV;
  • Your personal statement (300 to 500 words) summarising your background, skills and experience;
  • Your Undergraduate/Postgraduate Masters degree certificate(s) and transcript(s);
  • Evidence of your English language skills to IELTS 6.5 (or equivalent, 6.0 in all sections), if appropriate;
  • Contact details for TWO referees, one of which can be an academic member of staff in the College.

Please, submit all documents in one PDF file

Remember to state the title of the project at the top of your personal statement and in the email subject.

Meet the Supervisor(s) for this Studentship

Hamid Assadi - Prof Hamid Assadi is the Head of Virtual Engineering Centre and Professor of Solidification at Brunel University London. He studied Materials Engineering at Shiraz University, and received his PhD in Materials Science and Metallurgy from University of Cambridge in 1996. His work experience includes a professorship at Tarbiat Modares University, as well as several visiting appointments at Helmut Schmidt University, Max Planck Institute for Iron Research, and German Aerospace Centre (DLR).