Senior Scientist, SVS/DMD, ARTC
1 week ago
The Advanced Remanufacturing and Technology Centre (ARTC) is seeking a highly motivated Scientist / Senior Scientist to lead and conduct research in modelling, optimisation, and AI to solve complex industry challenges in the domain of enterprise multi-resource planning. This role is expected to drive scientific advances in operations research with the goal of translating innovations into impactful industry solutions for some of the largest MNCs and local companies. The role is a unique intersection of research, deployment-focused algorithm design, and business relevance. Key Responsibilities Lead research in rapid simulations, optimisation algorithms, and AI-enhanced workflows for enterprise multi-resource planning, primarily for the topics of production scheduling, operation planning, capacity planning, and sourcing and inventory analytics. Design practical algorithms to solve industry-inspired operational problems, performing solution benchmarking and discover novel market leading algorithms. Work with research engineers to translate research outcomes into deployable solutions for industrial applications. Formulate cutting edge scientific research directions and develop innovative research / project proposals aligned with industry needs. Drive competitive research funding proposals and collaborations with industry and academic partners (IHLs, other research institutes etc.). Contribute to the development of ARTC’s R&D roadmap in enterprise optimisation. Publish at leading international academic or industrial journals and conferences to contribute A*STAR's reputation as a leading R&D organisation. Mentor junior researchers in scientific methods and research professionalism. Requirements PhD in AI, Computer Science, Operations Research, or related disciplines with a clear emphasis on simulation, optimisation, machine learning, or data science. Deep understanding of production scheduling, operations planning, capacity planning, or sourcing & inventory analytics in the context of manufacturing. Strong background in modelling (e.g. discrete-event simulations), numerical optimisation (exact methods, meta-heuristics, surrogate-model-assisted methods) and AI as applied to solving various operations research problems. 3+ years of experience in post-PhD research. Demonstrated leadership or project management in relevant areas. Proven expertise in programming and algorithm development in Python. A track record of publications in top-tier journals and conferences. Excellent verbal and written communication skills, adaptable to various technical and non-technical contexts. Positive, proactive attitude, with the ability to influence and collaborate effectively across multiple stakeholders. #J-18808-Ljbffr
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