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.