About ARTC
The Advanced Remanufacturing and Technology Centre (ARTC) is a public-private partnership consortium with over 80 industry members. Our mission is to develop and deploy advanced manufacturing solutions and upskill the workforce to enhance local industry competitiveness.
Within ARTC, the Smart Virtual Systems (SVS) Group focuses on applying AI, optimisation, and advanced modelling techniques to enable intelligent, data-driven manufacturing operations.

About the Role

We are seeking a highly motivated Scientist / Senior Scientist to lead and conduct research in modelling and optimisation for enterprise multi-resource planning problems. This role offers the opportunity to advance scientific research in the field of operations research and translate innovations into impactful industry solutions across diverse range of industries.
The successful candidate will be an individual with a strong command of the required technical domains, leadership skills to manage research efforts and project delivery teams, excellent communication skills with technical and non-technical audiences, and good interpersonal skills to work with various stakeholders.  

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.

Job 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.
* 5+ 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).

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.