Singapore Institute of Technology (SIT) aspires to be a leader in innovative learning by integrating learning, industry and community. Its mission is to nurture and develop individuals who build on their interests and talents to impact society in meaningful ways.
Our engineering degree programmes have been developed through extensive consultation with industry, thus creating a curriculum that supports the industry’s needs in manpower development and innovation. We look forward to hiring faculty with industrial experience to foster applied learning in the courses being delivered.
SIT focuses on working with the industry on translational research and innovation, spanning technology readiness levels (TRLs) 3-7. Unlike traditional research which is discovery-oriented, applied research projects have higher TRLs which bridge the gap between theoretical research and industry applications. The SIT Engineering Cluster focuses on the five key pillars of Applied Research (i) Infrastructure (Maintenance) (ii) Sustainability (Built Environment) (iii) Energy Resilience (iv) Maritime Engineering and (v) Advanced Manufacturing, working with various industry sectors in Singapore as well as in the region.
For this faculty position in the area of microelectronics manufacturing, we are looking for candidates with the following essential and desirable skillset and competencies.
Essential
- Academic and industrial experience in semiconductor device fabrication and front-end processing
- Expertise in some or any of the areas of etching, lithography, in-line metrology and thin film technology (such as PVD, CVD ALD and ECP)
- Experience in reliability analysis, structure and surface analysis, and process characterisation
- Motivated to develop microelectronics manufacturing curriculum
- Strong demonstration of scholarship and commitment to teaching
Desirable
- Good knowledge in semiconductor packaging and back-end processing
- Experience in failure analysis and reliability testing in wafer fabrication, packaging, assembly and test
- Experience in emerging applications of machine learning and data analytics solutions for semiconductor manufacturing
- Preferred to have a PhD (in Electronics, Mechanical, or Manufacturing Engineering)
- Led or involved in strong industry - academia partnership projects
