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Field
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focuses on developing a cutting-edge hybrid quantum computing platform that integrates entangled superconducting qubits with single electron-on-neon (eNe) qubits. Key Responsibilities: Design and fabricate
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We are seeking a highly motivated postdoctoral researcher to join the Center for Nanoscale Materials (CNM) at Argonne National Laboratory. The successful candidate will contribute to the development
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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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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training in social science fields related to transdisciplinary agroecology, such as critical agrarian studies, sociology, political ecology, human geography, STS, and development studies. Experience with
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research and training in social science fields related to transdisciplinary agroecology, such as critical agrarian studies, sociology, political ecology, human geography, STS, and development studies
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Postdoctoral Research Associate- Training in the Design & Development of Infectious Disease Therapeu
research infrastructure at St. Jude to train top-tier postdoctoral fellows with the necessary skills to lead independent research programs in the discovery of infectious diseases therapeutics including small
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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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, and uncertainty-aware algorithms for autonomous driving systems. The position focuses on developing robust decision-making, planning, and control methods that address uncertainty arising from perception
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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