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apportionment, and related analytical methods (e.g., receptor models, chemical transport models, statistical analysis). Proficiency in programming languages (e.g., R, Python, MATLAB) and familiarity with relevant
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, or a related field. • Experience in research projects applied to urban, environmental, or socio-economic issues. • Proficiency in urban modeling tools such as MATLAB, Python (especially libraries
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. Develop workflows in R, Python, or MATLAB to process and analyze ocean color data, with a focus on chlorophyll-a, suspended particulate matter, and colored dissolved organic matter (CDOM). Assess the impact
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and simulation methods for energy networks. Experience with energy network analysis tools (e.g., MATLAB, Python, Simulink). • Publications: A strong record of publications in international scientific
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and regional scales. Proficiency in programming (e.g., Python, R) and experience with machine learning for geospatial data analysis. A strong track record of publishing research articles in high-impact
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., Python, R) and GIS tools (e.g., QGIS) experience. Excellent communication skills. Strong publication record related to current position
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machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency in Python and relevant
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multispectral/hyperspectral data processing. Proficiency in programming languages such as Python or R for data analysis and processing. Excellent communication skills and the ability to work effectively in a
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. Experience with handling large geospatial dataset using High performance computing (HPC) Advances programming (e.g., Python, R) and GIS tools (e.g., QGIS) experience. Excellent communication skills. Strong
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remote sensing and image analysis for soil fertility mapping. Experience with data fusion techniques and multispectral/hyperspectral data processing. Proficiency in programming languages such as Python