Research Scientist (Predicative Quality), IPV, ARTC

Job Description

Responsibilities:

·       Lead the research and development of novel AI architectures that fuse vision data with temporal manufacturing process data to predict final product quality.

·       Develop advanced methodologies for Root Cause Analysis (RCA), moving beyond correlation to establish causal links between process variables and inspection outcomes.

·       Design and implement Knowledge Graphs and semantic reasoning layers that integrate domain expertise with LLMs/VLMs to automate "final sentencing" and provide explainable AI (XAI) insights.

·       Architect and fine-tune state-of-the-art multimodal models to enable text-prompt able vision inspection and contextual decision-making.

·       Pioneer the use of Temporal Transformers or Physics-Informed Neural Networks (PINNs) to analyze complex manufacturing time-series data for anomaly detection and yield prediction.

·       Document research in high-impact internal reports or patent filings and stay at the forefront of AI/ML literature to maintain the institute competitive edge.

·       Provide technical oversight for QC/QA governance frameworks and mentor junior engineers in data integrity and model validation.

 JOB REQUIREMENTS

·       Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related quantitative field is mandatory.

·       Demonstrated experience in publishing or developing innovative algorithms in Computer Vision, Predictive Analytics, or Multimodal AI.

·       Deep understanding of AI-based image segmentation, classification and time-series analysis and signal processing.

·       Hands-on experience with Knowledge Graphs, ontologies, or graph neural networks (GNNs).

·       Strong background in Root Cause Analysis (RCA) and statistical process control.

·       Advanced Python programming skills.

·       Ability to drive research projects from conceptualization to a deployable "target product."

·       Exceptional ability to communicate complex scientific concepts to both technical peers and non-AI manufacturing stakeholders.