About this role
The NTI-NTU Corporate Laboratory is looking for a Research Fellow to conduct research in industrial AI for adaptive manufacturing process optimization and decision support. The role will develop and validate data-driven methods that combine production data, predictive and state-estimation models, uncertainty-aware learning and constrained optimization to improve process consistency under process drift and equipment variability.
Key Responsibilities:
• Develop machine-learning models for process prediction, state estimation, adaptive optimisation and closed-loop decision support. • Build structured and reproducible data pipelines for heterogeneous process, sensor, equipment, production and quality data. • Develop robust methods for process drift, sparse/noisy data and equipment variability, including uncertainty-aware modelling and transfer learning where appropriate. • Design constrained optimization and sequential decision strategies under engineering limits and human review. • Validate methods rigorously and translate research outcomes into prototypes, technical reports and publications.
Requirements:
• PhD in Computer Science/AI/Data Science or a relevant engineering or materials discipline. • Strong background in machine learning/data science, with hands-on Python and experience with modern ML frameworks. • Experience with one or more of time-series/sequence modelling, state estimation, constrained or Bayesian optimization, uncertainty-aware modelling, transfer learning or adaptive experimentation is highly desirable. • Experience with heterogeneous engineering or manufacturing data and rigorous model validation. • Manufacturing-process experience is advantageous; coating/thin-film or run-to-run process knowledge is a plus. • Strong research record, independent problem-solving ability and effective multidisciplinary communication skills.
We regret that only shortlisted candidates will be notified.
Hiring Institution: NTU