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University Chicago, in partnership with members of the Learning Theory team at Google. More information about IDEAL can be found here: https://www.ideal-institute.org/ . The positions will be hosted by
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, providing excellent opportunities for the prospective candidates to expand the professional network and acquire useful multidisciplinary skills and qualities. Please see our latest publication in Science
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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ESSENCE: Efficient Self-Supervised Machine Learning for Adaptive Wireless Communication Systems This project investigates self-supervised learning (SSL) for wireless communication systems to improve
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well as familiarity with machine learning workflows, natural language processing (NLP), and text-as-data methods. We are especially interested in applicants who demonstrate a strong substantive interest in using
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for all UNLV postdocs; and provide professional development programs and networking events for postdocs. UNLV currently employs postdoctoral scholars across a wide range of disciplines. Learn more about
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possible for up to 1 day/week. You will join an interdisciplinary team of researchers spanning imaging science, machine learning, genetics, and population health, working closely with collaborators
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-edge sensors with current computational tools and machine/deep learning. As a Postdoctoral Researcher, you will be a key member of our team responsible for designing the seamless integration
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infrastructures organized in infrastructure platforms, of which the Vibrational Spectroscopy Core Facility (ViSp) is a central infrastructure for this project (https://www.umu.se/en/research/infrastructure/visp