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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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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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postdoctoral researcher for pioneering research at the convergence of artificial intelligence (AI) and materials science. The ideal candidate should possess expertise in scientific machine learning (SciML
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
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: Develop novel machine learning theories and techniques for analyzing noisy time-series data, with a particular focus on seismic signals Perform uncertainty quantification in time-series analysis to assess
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] Subject Areas: Analytical Chemistry / Current Advances in Chemistry & Biochemistry Machine Learning / Machine Learning Computational Science and Engineering / Machine Learning Artificial Intelligence
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] Subject Areas: Theoretical Physics / Statistical physics Complex Systems Machine Learning Computational Science and Engineering / AI/ Machine Learning , Artificial Intelligence , Data Sciences , Machine
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an emphasis on technology, data science and the humanities. LKCMedicine is searching for a Research Associate/Fellow in the field of Learning Analytics (LA) / Machine Learning (ML) under Professor Andy Khong
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models