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spans quantum mechanics, statistical physics, and deep learning and aims to enable AI-guided predictions of synthesizable and functional materials such as energy storages, catalysts, smart-alloys, energy
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Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, can be found at https://postdoc.hms.harvard.edu/guidelines Minimum Number of References Required Maximum
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: Education: Bachelor in Biosciences, or Engineering degree in Computer or Data Sciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: previous experience working with
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, and the ability to read related scientific papers on cancer combination therapy. It would also require expertise in relevant AI methodology, such as deep learning architectures for property prediction
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will have completed a PhD in biochemistry, molecular biology, microbiology, structural biology, synthetic biology, or a closely related discipline. You will enjoy contributing to impactful research
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/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) and radiomics preferred
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Expertise: Familiarity with supervised/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g
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disciplines associated with geography, soil sciences, hydrology, civil engineering, or related discipline, with research expertise in geospatial AI, deep learning foundation models, hydrology, river science
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to support the growth and competitiveness of our automotive designs in the global landscape. For more details, please view https://www.ntu.edu.sg/ancl Key job purpose Conduct investigations on the bonding and
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opportunities for growth in the field of clinical research. To learn more about the research program, please visit: https://www.limbnetwork.com/home RESPONSIBILITIES Reporting to Dr. Anthony Cooper