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Field
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background in (Python) programming. Strong interest or experience with (geospatial) data science. Eager to conduct field experiments that are rooted in electromagnetic theory. Organizational skills, and
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shocks and stressors). This research effort relies extensively on modeling and optimization, with consideration for field data collection, and statistical and geospatial data analysis. Informed by
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geospatial data as well as can travel, and work from large and small vessels. Preferred Qualifications Valid Driver’s License. Proficiency with one or more Programming Languages/Platforms/Libraries such as C
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-scale geospatial and mixed-mode data. Ability to compile, clean, wrangle, visualize, and manage large amounts of tabular and geospatial data; GIS. A solid foundation in applied statistics (i.e. regression
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longitudinal data in the context of a research study. Experience with advanced methods (e.g., multiple imputation, propensity scores, geospatial analyses, etc.), manuscript and presentation preparation
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protocols such as TCP/IP. Familiarity with interpreted scripting languages such as Python or Bash. Knowledge of database concepts and/or familiarity with SQL. Exposure to geospatial analysis and GIS concepts
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data processing and geospatial technologies to the forefront of scientific discovery. Through our research and teaching, our faculty, staff, and students advance cutting-edge solutions for climate change
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of the demand of geospatial information in the field of smart cities Documentation, and improvement of deep learning algorithms for real-world applications Communication and between other project members, the TUM