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to the fundamentals of spatiotemporal data science and machine learning using scripting languages. Supervise BSc and MSc thesis students conducting research in Geo-information Science. You will work here The research
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an excellent work ethic and background in molecular simulation and machine learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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and machine learning models. To be successful in this role, you will have excellent communication skills and written English, strong quantitative and analytical skills, the ability to work creatively
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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scientists, and machine learning experts will be an essential and enriching component of the position. Strong candidates will have a background in machine learning and natural language processing (NLP), with a
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 9 hours ago
. Description: This project aims to develop a next-generation wildfire risk assessment platform that tightly integrates Earth Observation (EO) data, deep learning, and dynamic fire behavior modeling
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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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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and