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Field
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highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
Requisition Id 15685 Overview: The Center for Nanophase Materials Sciences (CNMS) is seeking a Postdoctoral Research Associate to support research directed towards developing novel AI/ML algorithms
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, the objective of this thesis is to develop and test a real-time, tightly-coupled traversability algorithm that fuses information from both sensor modalities, therefore providing a more complete understanding of
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actors. The developed algorithms will be validated using simulation testbeds and simple hardware-in-the-loop microgrid setups with battery storage. Overall, this research will advance the state of the art
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, regionally anchored top university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world's most pressing issues. In research and academic programs
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, regionally anchored top university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world's most pressing issues. In research and academic programs
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multi-agent pathfinding (MAPF) algorithms - Experience across multiple areas is a strong plus; Experience developing ML-based optimization approaches is a strong plus; A strong publication track record is
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for various research projects, ensuring smooth workflow and minimizing downtime. Uses automation intelligence tools to build functional operational systems. Builds algorithms to integrate multiple robotics
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multiple domains—FMECA, sensing technologies, data fusion, data analytics, and machine learning. Combining these diverse skill sets to develop a unified and reliable solution can be technically demanding
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-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments