Research Fellow (Industrial AI for Manufacturing Process Optimization)

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