Deskripsi Pekerjaan
Are you a visionary researcher looking to push the boundaries of Industry 4.0? The Agency for Science, Technology and Research (A*STAR) is seeking a Senior/Lead Research Engineer to join our Advanced Remanufacturing and Technology Centre (ARTC). In this pivotal role, you will be at the forefront of the Digital Manufacturing Division (DMD) and Smart Value Stream (SVS) initiatives, leveraging AI and machine learning to revolutionize manufacturing planning and operational analytics.
We are looking for a technical leader who can bridge the gap between cutting-edge academic research and industrial-scale deployment. You will lead multidisciplinary projects to transform raw production data into actionable intelligence, optimizing factory throughput, energy efficiency, and predictive maintenance. This is an exceptional opportunity to influence Singapore’s manufacturing landscape and work alongside world-class scientists in a state-of-the-art facility.
Tanggung Jawab
- Lead the design and implementation of AI/ML models to solve complex manufacturing planning and scheduling challenges.
- Architect scalable data analytics pipelines for real-time factory floor monitoring and decision-making.
- Collaborate with cross-functional teams to integrate predictive maintenance systems within existing production workflows.
- Provide technical mentorship to junior research engineers and guide them in best practices for AI software development.
- Translate business-critical manufacturing problems into innovative research projects with measurable KPIs.
- Engage with industry partners to pilot new technologies and drive the adoption of smart manufacturing solutions.
- Publish technical white papers and patent research outcomes to maintain ARTC’s leadership position in the global engineering community.
Kualifikasi
- Master’s or PhD in Computer Science, Data Science, Industrial Engineering, or a related technical discipline.
- Minimum 5+ years of experience in AI, machine learning, or operations research, ideally within a manufacturing environment.
- Proven track record of deploying machine learning models into production environments.
- Advanced proficiency in Python, C++, or Java and deep expertise with frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Strong background in optimization algorithms, heuristic modeling, or discrete event simulation.
- Experience with big data technologies (Spark, Hadoop) and cloud-based deployments (AWS/Azure/GCP).
- Strong leadership capabilities with the ability to manage complex project timelines and stakeholder expectations.