A*STAR Advanced Remanufacturing and Technology Centre (A*STAR ARTC)

Through a blend of simulation, machine learning and advanced data analytics, digital twins are emerging as one of AI’s most powerful applications, transforming innovation cycles across a diverse range of industries.

In recognition of their research excellence, leadership and impact both locally and globally, ten leading A*STAR researchers have been awarded the A*STAR Distinguished Fellowship and Fellowships.

From innovative medical devices to advanced manufacturing partnerships, the work of A*STAR recipients of the 2024 President’s Science and Technology Awards exemplify the importance of translating research into clinical and commercial outcomes.

Through data-driven approaches, Ginny Lee is helping industries tackle the sustainability challenges of decarbonisation and greener supply chains.

Cell and gene therapies are driving the next revolution in medicine, combining the best of biology with game-changing technologies from A*STAR researchers and partners.

In recognition of their scientific contributions and impact both locally and globally, 14 leading researchers have been awarded the 2024 A*STAR Fellowship.

Through advanced chemistries in materials and process engineering, A*STAR lends its support to national development goals for a more circular economy.

From production to storage and usage, A*STAR research institutes are tackling challenges in sustainable energy to meet the needs of the 21st-century.

By contributing deep insights to Singapore’s innovation ecosystem, A*STAR initiatives are helping to build strategic relationships with international partners and bridge the gaps in local capabilities.

New equations and methodology enable the uncertainty of porosity measurements to be evaluated in additively manufactured metals for safer, high-quality components.

A specially designed computational model guides the development of next-generation metal welding processes by predicting how heat affects the strength of welded joints.

A novel machine learning model predicts wear and tear in manufacturing parts with less real-world data for cost savings and improved productivity.