The Digital Supply Chain Group of the Resilient Value Chain Division at ARTC is searching for a Senior Research Engineer to lead and support engineering projects focused on operations research using advanced analytics, simulations, and optimization.
Responsibilities:
Innovate and Develop New Solutions:
Design and prototype novel algorithms, tools, and systems to solve complex operational and industrial challenges.
Conduct research and feasibility studies to create innovative, scalable, and efficient engineering solutions that meet industry needs.
Lead the Development Process:
Lead end-to-end development of research concepts into functional engineering prototypes, ensuring they are industry-ready.
Collaborate with cross-functional teams to transition research results into tangible, deployable products or solutions.
Apply Advanced Research in Engineering:
Leverage cutting-edge technologies such as simulations (discrete-event & agent-based), network analysis, and optimization techniques to develop solutions.
Implement machine learning and AI technologies into operational processes to enhance decision-making capabilities.
Conduct Performance Testing and Optimization:
Evaluate the performance and scalability of developed solutions through testing and optimization.
Continuously improve prototypes by analyzing performance data and making necessary adjustments.
Collaborate with Industry Partners:
Work closely with industry partners to ensure that developed solutions align with real-world applications and requirements.
Engage with stakeholders to gather feedback and refine research prototypes for successful industry adoption.
Job Requirements:
- Bachelor or Master Degree in Engineering, Computer Science, Operations Research, or related fields.
- At least 5 years of experience
- Strong foundation in mathematical modeling, algorithm design, and scientific computing.
- Expertise in simulations (discrete-event, agent-based) and optimization techniques.
- Proficiency in programming languages such as Python, C, C++, or MATLAB.
- Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and optimization libraries (e.g., Gurobi, CPLEX).
- Experience in data structures, algorithm development, and software development best practices.
- Prior experience in operational optimization, such as fleet management, scheduling, or network design, is highly preferred.
- Experience applying research in practical settings such as aerospace, logistics, energy, manufacturing, or supply chain management.
- Familiarity with Industry 4.0 technologies such as advanced robotics, automation, or smart manufacturing.
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.
