Research Fellow (Mechanical Engineering / Nanotechnology)

About the role

The Singapore Centre for 3D Printing (SC3DP) at NTU seeks a highly motivated Research Fellow to advance metal multimaterial additive manufacturing. The fellow will develop a novel laser powder‑bed fusion system, focusing on automation, control, and in‑process monitoring.

Key responsibilities

• Conduct systematic experimental campaigns in multimaterial LPBF, generating data for model development and process consolidation.
• Develop physics‑based and data‑driven modeling frameworks that integrate in‑situ monitoring, process parameters, and material combinations.
• Design and optimise functionally graded LPBF components, including lightweight lattices, using DfAM guidelines.
• Perform studies on powder‑bed behaviour, melt‑pool dynamics, and inter‑material transition zones.
• Analyse results to establish robust process‑material‑structure relationships and support optimisation strategies.
• Support automation, control, and monitoring development for the multimaterial LPBF platform.

Required profile

• PhD in Mechanical Engineering, Nanotechnology, or a closely related discipline.
• Strong background in mathematical modelling of multiscale and multiphysics phenomena, including dimensional and fractal analysis.
• Proven ability to interpret complex additive‑manufacturing behaviour using fractal descriptors.
• Proficiency in advanced mechanical design, DfAM, and topological optimisation of lattice structures.
• Experience implementing AI and data‑driven methods (ANNs, evolutionary algorithms) with programming skills in Matlab or Python.
• Hands‑on experience with LPBF, FDM, SLA/DLP, and electrohydrodynamic printing processes.
• Experience in materials characterisation (microstructural, mechanical, thermal, surface, electrical).
• Ability to lead complex research tasks, manage multiple workstreams and communicate results to academic and industrial stakeholders.

Required skills

• Mathematical modelling, dimensional analysis, fractal analysis, hybrid physics‑AI approaches
• Design for Additive Manufacturing (DfAM), topological optimisation
• CAD tools: Fusion 360, SolidWorks, nTop
• Artificial‑intelligence methods: ANNs, evolutionary genetic algorithms
• Programming: Matlab, Python
• Additive manufacturing workflows: LPBF, FDM, SLA/DLP, e‑jet printing, electrospray, electrospinning
• Materials characterisation: microstructural, mechanical, thermal, surface, electrical assessments