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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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/Administrative Internal Number: 7319007 University of California Irvine Postdoctoral Researcher/Lab Scientist Deep Phenotyping Position overview Salary range: The salary range for this position is $66,737-$82,836
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where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a
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activities. This is an exciting opportunity to work within a collaborative group with deep expertise in silicon detectors, Trigger/DAQ (TDAQ) systems, software, and computing. The Argonne ATLAS group plays a
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or triggers for the ATLAS experiment would be highly desirable, as would experience with deep learning and concurrent programming The post will work in the first instance the High Energy Physics Group
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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integrate calculations of quasielastic and deep-inelastic scattering processes into a unified framework for modeling electron-nucleus cross-section, and evaluate reaction and nuclear-structure models relevant
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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have skills in eukaryotic cell biology, electron microscopy, and bioimage analysis. You have a basic knowledge in integrative structural biology, and in AI / deep learning approaches and/or sub-tomogram