Research Associate (ElectricalElectronics Engineering Computer Science)

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. SC3DPs 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 Associate 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 NTUs commitment to sustainable and impactful technological development.

Key Responsibilities:

• Conduct data acquisition campaigns collecting synchronized vision and sensor data from the machine to support model training testing and validation enabling reliable model development and improvement during the ML development lifecycle.

• Development and implementation of data-driven computer vision and machine learning models using sensor data camera feedback and process parameters for print and tool path planning and process optimisation.

• Deploy real-time monitoring and adaptive control embedded systems with multi-sensors and suitable control/machine-learning algorithms to enable automated feedback loops that improve print quality.

• Conduct experimental studies and performance evaluations analysing vision system accuracy latency and impact on manufacturing efficiency and product quality.

• Prepare scientific manuscripts presentations and technical reports.

Requirements:

• Masters degree in Electrical/Electronics Engineering Computer Science or a closely related discipline.

• Strong foundation in software engineering for intelligent systems including machine/deep learning embedded systems and computer vision.

• Strong industrial experience delivering computer visionbased applications including embedded system integration real-time performance optimization and deployment in real-world environments.

• Familiarity with the full machine learning development lifecycle (e.g. data preprocessing model training evaluation and fine-tuning) combined with sensor fusion expertise (RGB Depth Cameras) and deployment of machine learning models on embedded/edge systems for real-time industrial applications.

• Proficiency in Python and C/C with hands-on experience using deep learning frameworks (e.g. TensorFlow/PyTorch) and embedded systems for vision-based system development.

• Project management and collaboration skills leading complex technical tasks and effectively communicating research outcomes to multidisciplinary teams and stakeholders.

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