PhD Candidate in Advanced Shape Memory Materials and Additive Manufacturing

About the role

This PhD research investigates process-structure-property relationships in advanced shape memory materials, including shape memory alloys (SMAs) and shape memory polymers (SMPs), processed by additive manufacturing techniques such as Selective Laser Melting / Laser Powder Bed Fusion (SLM/LPBF), Selective Laser Sintering (SLS) and Fused Deposition Modeling (FDM). The project aims to establish local control over phase transformations, chemical composition and microstructural grading, using machine learning to model high-dimensional parameter spaces and predict thermo-mechanical behaviour and shape memory effects. The ultimate goal is a unified, data-driven framework for functionally graded smart materials for 4D printing, with applications in biomedical, aerospace and soft robotics.

Key responsibilities

• Design and run additive manufacturing trials (LPBF/SLM, SLS and FDM) for shape memory alloys and polymers.
• Characterize thermal, mechanical, microstructural and shape memory properties (e.g. DSC, SEM, XRD, DMA).
• Develop machine learning models and data-driven frameworks linking manufacturing parameters to material response.
• Analyse experimental and computational datasets to drive multi-material and multi-process innovation.
• Publish at least four first-author peer-reviewed journal articles and present at international conferences.

Required profile

• Master's degree in Materials Science Engineering, Mechanical Engineering, Polymer Engineering, Additive Manufacturing or a related field.
• Strong background in materials science, physical metallurgy, polymer science and/or smart materials.
• Practical or theoretical knowledge of SLM/LPBF, SLS or FDM.
• Mandatory English proficiency; IELTS or TOEFL certificates are highly desirable.
• Strong scientific writing and commitment to high-impact publishing.

Required skills

• Knowledge of machine learning, data analysis and computational materials modelling.
• Materials characterization techniques such as DSC, SEM, XRD and DMA.

Applications are made through the university website, with a deadline of 25 October.