Early Career Awards (ECA)

The Early Career Awards (ECA) are designed to support Singaporeans who have recently completed their postdoctoral training and prepare them for an academic career at SIT. This award aims to enhance the teaching capabilities of the recipient and help build a strong research portfolio.

Eligibility and Selection Criteria

• Singapore Citizen
• Recently completed postdoctoral training
• Excellent academic record plus a high degree of proficiency and motivation for a research area that is strongly aligned with SIT’s supported areas
• Strong passion for teaching, research and an academic career

Benefits

Awardees may receive research grants to help build a competitive research portfolio. Upon completion of the award, awardees will be considered for Assistant Professorship.

Awardees will also be assigned a faculty mentor for the duration of the scheme.

Application Process

• Applications are open throughout the year. Applications received before 15 January 2026 will be considered for intake 2026.
• Interview by the respective clusters for shortlisted applicants will be conducted throughout the year.
• Final Interview by Panel for shortlisted applicants will take place from March to April 2026.
• Awards for intake 2026 will be finalised by July 2026.

Supported Areas for START

Food, Chemical and Biotechnology

• Sustainable and Functional food:Sustainable food systems & circular food economy - alternative proteins, upcycled ingredients); AI & automation in food manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain
• Biotechnology:Host strain screening and engineering for microbial bioprocess development; microbial physiology and adaptation for industrial applications; high-throughput screening and automation for strain optimization; bioprospecting for novel microbial hosts, enzymes, and metabolites to support sustainable biomanufacturing.
• (Bio)pharmaceuticals:Processing science and technology for bio- and chemical transformations, separations, purifications and formulation applied to pharmaceuticals manufacture; modelling of processing steps and whole processes to support innovation, design, optimisation and control for manufacturing operations.
• Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital twins and real-time process monitoring and control; process analytical technology (PAT); process intensification and sustainability.