PhD scholarship in ‘Structures and Materials': Defect Analysis and Fatigue Life Prediction in Additively Manufactured Alloys using Machine Learning

This project will deploy artificial intelligence and deep learning to evaluate structural integrity and predict fatigue life of additively manufactured aerospace metal alloys.

To address this limitation, this project aims to develop an innovative fatigue damage model that incorporates detailed defect characteristics using machine learning and multiscale modeling. High-resolution X-ray computed tomography (CT) will be employed to observe and quantify the dynamic changes of defects in AM Ni-based alloys with varying porosities and printing orientations during fatigue testing. 


Open now. 

Closes 31st December 2026. 

One scholarship available. 

Master by Research degree; or a Master by Coursework degree with a significant research component graded as high distinction or equivalent; or a Bachelor Honours degree achieving first class honours; in Engineering (Aerospace, Mechanical, Materials, Manufacturing), or Science (Physics, Chemistry), or another suitable field; 

To apply, please submit the following documents to Professor Raj Das (

  • A cover letter briefly outlining your interest in the project
  • Evidence of research ability, such as a digital copy of a Master’s or Honours’ thesis
  • A digital copy of academic transcripts

A CV including any education, marks/grades, relevant professional experience, publications (if any), awards (if any), and names of two referees.

Knowledge and Skills: Knowledge and background in either solid mechanics, material engineering or finite element analysis is desireable.

Please contact Professor Raj Das (

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