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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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with modern machine learning and AI technologies to effectively address large-scale problems. About the research project We are seeking a highly motivated Postdoc to join our group in developing
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at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning
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of the postdoctoral scholars. https://postdoc.ucsd.edu/postdocs/px-contract.html Qualifications Basic qualifications (required at time of application) Doctorate degree or equivalent terminal degree in Engineering
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, machine learning, statistics and programming skills (R and Python) is preferred. Record of peer-reviewed publications. Knowledge in one or more of the following areas is desirable: single-cell profiling
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high