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The Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is seeking a Postdoctoral Researcher – Scientific Machine Learning & Computational
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(within the past five years), or be close to completion of a PhD in a relevant field such as data science, AI, computer science, remote sensing, machine learning, electronics, embedded systems, aerospace
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
. Candidates with expertise in related areas, such as lattice field theory algorithms, machine-learning methods for lattice field theory, hadron spectroscopy, or hadron-structure phenomenology, are encouraged
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their added value against simpler machine-learning baselines; train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies
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against simpler machine-learning baselines; • train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies and
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collection, data engineering, statistical modeling, or computational text and image analysis; Machine learning, natural-language processing, large language models, or evaluation and auditing of AI and online
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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initiation latency, movement speed, gait characteristics, postural control, motor variability, and other behavioural descriptors. ESSENTIAL REQUIREMENT PhD in machine learning, artificial intelligence
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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications