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  Application of artificial intelligence to multiphysics problems in materials design

   Department of Civil Engineering

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  Dr Bahman Ghiassi, Dr A Alexiadis  Applications accepted all year round  Funded PhD Project (European/UK Students Only)

About the Project

Development, design or assessment of materials with better performance, better durability and lower carbon footprints are among the main current challenges of many engineering disciplines including the construction sector. Experimental or computational modelling approaches have been traditionally used for this purpose. The experimental approaches are often based on trial-and-error methods, which are lengthy, prone to human errors and suffer from a lack of generality for application to other materials. Meanwhile, computational modelling approaches are often complex, time-consuming, and computationally expensive, while requiring the development of complex Multiphysics models and mesh strategies with appropriate initial and boundary conditions.

Development of innovative approaches that allow the rapid setting up of such complex models based on limited data or to guide the design space can be a step change in how we design engineering materials and is the main objective of this fully funded PhD project. The project will mainly involve computational modelling and machine learning but experimental testing can also be included if needed.

During the course of this project, you will have the opportunity to work on a highly innovative subject with a multidisciplinary team of scientists from different disciplines. You will also receive extensive hands-on training on advanced materials modelling and machine learning approaches, and you will have the opportunity to interact with industrial partners.

We are seeking an enthusiastic and highly motivated home/UK student with good interpersonal skills and a keen interest in research. You must have, or expect to achieve, at least a 2:1 honors degree or a distinction or high merit at MSc level (or international equivalent) in Civil Engineering, Chemical Engineering, Mathematics or Machine Learning. The candidate will be expected to have good interpersonal skills with a teamwork spirit. The selection will be made as soon as a suitable candidate is identified and based on a detailed review of the CV and an interview.

 For further details and discussion about the position, please contact Dr Bahman Ghiassi ([Email Address Removed]) or Dr. Alessio Alexiadis (.[Email Address Removed]).

Engineering (12)

Funding Notes

Funding will be available to UK/Home students only on a competitive basis.

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