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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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Algorithms for Agro-industrial, Health, and Environmental Sustainability, within the FAPESP Center for Research and Innovation on Smart and Quantum Materials (CRISQuaM). The project aim to develop quantum
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to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be
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allows the successful applicant to choose a flexible balance between advanced machine learning method development and exciting applications on molecular data, including biomedicine and drug discovery
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 7 hours ago
fields including health, agriculture and ecology, sustainable development. More information, please visit https://team.inria.fr/scool/projects Odalric-Ambrym Maillard is a permanent researcher at Inria. He
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wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences
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to the global mental-health burden, and your work will directly inform the development of personalized, adaptive digital interventions. You will be embedded in ongoing research programs led by Dr. Jonas Everaert
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classical and quantum modeling, quantum algorithms and computation, and battery materials and technology Further development of your personal and interdisciplinary strengths through a range of training
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University, to begin as early as July 1, 2025. Topics include the experimental quantum simulation of chemical and condensed-matter systems using 1D and 2D ion arrays, and the development and optimization
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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