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-electron Schrödinger equation for fermions and bosons with high accuracy and on the application of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning
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methodologies generally, machine learning techniques, OR complexity analysis/nonlinear dynamics are particularly well-matched to the opportunity, but applicants with theoretical expertise related to compact
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methodologies generally, machine learning techniques, OR complexity analysis/nonlinear dynamics are particularly well-matched to the opportunity, but applicants with theoretical expertise related to compact
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time will be allocated to teaching or supervision duties. Requirements The successful applicant should have a doctoral degree in statistics, mathematics, machine learning, or other relevant field, and
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is currently building and commissioning a network of calibrated multi-sensor observatory-class systems, and developing novel machine learning methods with the aim of collecting science-quality data
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minimum qualifications at the time of hire. PhD in Computer Science, Artificial Intelligence, Human-computer Interaction, Computational Linguistics, Machine learning, Biomedical Informatics, or related
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array
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University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
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, United States of America [map ] Subject Areas: Statistics, Machine Learning, and AI Appl Deadline: 2025/07/01 11:59PM (posted 2024/10/31, listed until 2025/07/01) Position Description: Position Description The University
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spanning multiple diseases. About the lab: The Glastonbury Lab is focused on developing and applying Machine Learning to problems in digital pathology and spatial transcriptomics. The group has a particular