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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most
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the FORLUX (https://www.list.lu/en/research/project/forlux ) research project, both of which together will include 13 doctoral candidates and 4 postdoctoral researchers. We seek candidates with a strong
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DEPARTMENT The Department of Electrical and Computer Engineering at UTEP (http://ece.utep.edu) offers Bachelor of Science (B.S.) and Master of Science (M.S.) degrees in Electrical Engineering and in Computer
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the collective bargaining agreement with the Graduate Labor Union-United Electrical, Radio and Machine Workers of America (GLU-UE). Research Assistant Duties and Responsibilities. This position works closely with
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-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated evidence of excellent programmin g, collaboration
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experiments. Data & Analysis: • Collaborate with data scientists to analyze host and microbial data using statistical, bioinformatic, or machine learning approaches. • Contribute to the integration of spatial