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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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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: Computational Biology / Data Analytics Analytical Chemistry / Current Advances in Chemistry & Biochemistry Machine Learning / Machine Learning Computational Science and Engineering
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at the Institute of Genetics and Cancer. Informal enquiries may be directed to Dr Athina Spiliopoulou (A.Spiliopoulou@ed.ac.uk ). Your skills and attributes for success: PhD in machine learning, genetic epidemiology
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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
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experience using Python machine learning and large language models. Experience in machine learning and NLP for automated misinformation detection, social media data scraping and analysis, and human annotation
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, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate
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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