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generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical
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generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical
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large multi-dimensional datasets using statistical tools such as positive matrix factorization (PMF) and cluster analysis Investigate the influence of different urban emission sectors on atmospheric
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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lives, one discovery at a time! Community Information Columbia offers small-town friendliness with big city features and a high quality of life for people of all ages and interests. Founded on education
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. We will develop an isotope version of a process-based CH4 model and update the representation of different wetland types in the model using a data inversion approach. Additionally, we will analyze
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
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variables related to the weather conditions, road surface micro- and macro-texture, tire properties, driving speed, and behaviours of road users. This project aims at studying different data sources such as
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efficient and scalable algorithms that can handle large-scale datasets. Tensor Analysis: Analyze the structure and properties of multidimensional networks represented as tensors. Investigate different
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emulation software for FPGA-based electronics. • Experience contributing to data taking of large experiments. Additional Information: Application Instructions Applications should include: • A curriculum