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- Faculty of Science, Charles University
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to climate change and variability Hydrological processes in organosols and peat-affected soils Modeling Hydrological Extremes Using Machine Learning Spatial and time distribution of precipitation within
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educator in statistics and/or modern data analysis (including ML/DL). Research scope – expertise in any of the following areas • statistics, data analysis, and information theory, • machine learning, deep
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Machine Learning. Profile of the graduate The graduate displays deep theoretical knowledge in molecular and cell biology, genetics and virology, with focus on some specific branch of these scientific fields
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of dissertation topics: Developing Remote Sensing–Based Indicators of Landscape State and Change Using Data-Efficient Machine Learning Across Scales Profile of the graduate The graduates have deep theoretical
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: an insight from Genetics, Single-Cell Transcriptomics, and Machine Learning. Profile of the graduate Ph.D. graduate has extensive knowledge of cell and developmental biology, ranging from basic principles