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Open nowPosted today

Research Fellow (Industrial AI for Manufacturing Process Optimization)

MyCareersFuture97,045 open roles

Pay
SGD 6,000 – SGD 12,000 a month
Where
West, Singapore
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Your applicationOpen nowResearch Fellow (Industrial AI for Manufacturing Process Optimization)MyCareersFuture · West, Singapore

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This job: posted today

MyCareersFuture median: 3 days open

The posting

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.

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