A*STAR

Scientist (Senior), DMD/SVS, ARTC

A*STAR
ResearchSingaporeFull-time1 months ago

About the role

AI summarised

The Advanced Remanufacturing and Technology Centre (ARTC) seeks a Scientist/Senior Scientist to lead research in modelling and optimisation for enterprise multi-resource planning. The role involves developing innovative optimisation algorithms, translating research into industrial solutions, and publishing in top journals. Candidates should have a PhD in AI, Computer Science, or Operations Research with strong background in optimisation and machine learning.

ResearchFull-time

Key Responsibilities

  • Lead research into innovative optimisation algorithms for multi-resource planning, including production scheduling, inventory management, and capacity planning.
  • Work with engineers to translate research outcomes into deployable solutions for industrial applications.
  • Formulate scientific research directions and develop innovative proposals aligned with industry needs.
  • Drive competitive research funding proposals and collaborations with industry and academic partners.
  • Publish high-quality research in top international journals and conferences.
  • Mentor junior researchers and contribute to the development of ARTC's R&D roadmap in enterprise optimisation.

Requirements

  • PhD in AI, Computer Science, Operations Research, or related disciplines with emphasis on optimisation, machine learning, or data science.
  • Strong background in modelling and optimisation for planning & scheduling problems.
  • 3+ years' experience in R&D leadership and research project management in relevant areas.
  • Proven expertise in programming and algorithm development (e.g., Python, C# .NET) for optimisation and machine learning.
  • Strong track record of publications in top-tier journals and conferences.
  • Deep understanding of production planning, scheduling, and inventory optimisation domains.
  • Excellent technical writing, presentation, and communication skills.
  • Demonstrated ability to apply optimisation algorithms to real-world multi-resource planning problems (industry projects experience is a plus).