{"id":1253604,"url":"https://alion.io/job/astar-research-scientist-predicative-quality-ipv-artc","title":"Research Scientist (Predicative Quality), IPV, ARTC","company":{"id":50235,"name":"Agency for Science, Technology and 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AI","optional":false},{"name":"Image Segmentation","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-09-25T18:38:00Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-18T07:19:58Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"Job Description\nResponsibilities:\n· Lead the research and development of novel AI architectures that fuse vision data with temporal manufacturing process data to predict final product quality.\n· Develop advanced methodologies for Root Cause Analysis (RCA), moving beyond correlation to establish causal links between process variables and inspection outcomes.\n· 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.\n· Architect and fine-tune state-of-the-art multimodal models to enable text-prompt able vision inspection and contextual decision-making.\n· 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.\n· 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.\n· Provide technical oversight for QC/QA governance frameworks and mentor junior engineers in data integrity and model validation.\nJOB REQUIREMENTS\n· Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related quantitative field is mandatory.\n· Demonstrated experience in publishing or developing innovative algorithms in Computer Vision, Predictive Analytics, or Multimodal AI.\n· Deep understanding of AI-based image segmentation, classification and time-series analysis and signal processing.\n· Hands-on experience with Knowledge Graphs, ontologies, or graph neural networks (GNNs).\n· Strong background in Root Cause Analysis (RCA) and statistical process control.\n· Advanced Python programming skills.\n· Ability to drive research projects from conceptualization to a deployable \"target product.\"\n· Exceptional ability to communicate complex scientific concepts to both technical peers and non-AI manufacturing 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