The Singapore Centre for 3D Printing (SC3DP) is a research centre conducting fundamental and applied research in all aspects related to additive manufacturing. Our vision is to be the world leader in 3D Printing and a wellspring of knowledge, delivering state of the art and innovative solutions to the industry. SC3DP is at the forefront of developing advanced technologies and high-impact research in areas such biomedical, construction, aerospace, marine and offshore. It is one of the very few research centres globally with multi-sector 3D printing expertise. SC3DP’s research achievements, high-level industry collaboration, comprehensive facilities and resources have positioned it as a leading hub for 3D printing research and innovation globally.

We are looking for a highly motivated Research Fellow to join our multidisciplinary team aiming to develop the next generation of metal multimaterial additive manufacturing. The successful candidate will work on the development of a novel laser powder bed fusion system, focusing on automation and control aspects of this novel machine and the implementation of an in-process monitoring system. This role is central to our efforts to push the boundaries of AM material innovation and supports NTU’s commitment to sustainable and impactful technological development.

Key Responsibilities:

• Conduct systematic experimental campaigns in multimaterial LPBF, generating process, material, and structural data to support model development, validation, and process consolidation.

• Develop and apply physics-based and data-driven modeling frameworks to describe, predict, and optimize multimaterial LPBF processes, integrating in-situ monitoring, process parameters, material combinations, and resulting microstructural and structural outcomes.

• Design and optimize multimaterial and functionally graded LPBF components, including lightweight lattice and architected structures, incorporating DfAM guidelines and performance-driven criteria.

• Perform experimental studies to evaluate powder bed behaviour, melt pool dynamics, and inter-material transition zones, assessing their impact on process stability, densification, and component performance.

• Analyse experimental and modeling results to establish robust process–material–structure relationships and support informed optimization strategies.

• Support automation, control, and in-process monitoring development for a novel multimaterial LPBF platform.

• Document research findings, prepare technical reports, and contribute to publications and presentations for internal and external stakeholders.

• Support project planning, timelines, and delivery, ensuring milestones are met and solutions meet industrial standards for quality and safety.

Requirements:

• PhD in Mechanical Engineering, Nanotechnology, or a closely related discipline.

• Strong background in the mathematical modeling of multiscale and multiphysics phenomena, including proven application of dimensional analysis, fractal analysis, and hybrid physics–AI approaches.

• Demonstrated ability to interpret complex process behaviour through fractal descriptors across additive manufacturing technologies.

• Proficiency in advanced mechanical design, including DfAM and topological optimization of lattice structures, supported by practical experience with CAD platforms such as Fusion 360, SolidWorks, and nTop.

• Experience in the implementation of artificial intelligence and data-driven methods for additive manufacturing applications, including ANNs and evolutionary genetic algorithms for process optimization supported by programming knowledge (e.g. Matlab, Python).

• Hands-on experience with advanced additive manufacturing workflows, including LPBF, FDM, SLA/DLP, and electrohydrodynamic-based processes (e-jet printing, electrospray, electrospinning).

• Experience in materials characterization, encompassing microstructural, mechanical, thermal, surface, and electrical property assessment.

• Ability to lead complex research tasks, manage multiple workstreams, and communicate technical results effectively to multidisciplinary academic and industrial stakeholders.

We regret to inform that only shortlisted candidates will be notified.