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4 дня назад

Satellite Applications Specialist (Precipitation)

90 000 - 150 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR

Satellite Applications Specialist (Precipitation): Processing and analyzing satellite cloud and precipitation data for cloud seeding validation with an accent on remote sensing and meteorological processes. Focus on developing advanced data processing algorithms, quantitative precipitation estimation, and implementing machine learning methods.

Location: On-site in El Segundo, CA

Salary: $90,000 - $150,000 a year

Company

hirify.global is pioneering a modern cloud seeding system to solve water scarcity and inclement weather problems using radar validation and UAS.

What you will do

  • Acquire and manage large multi-spectral datasets from platforms such as GOES, Meteosat, MODIS, and Sentinel.
  • Implement atmospheric correction algorithms and develop workflows for quantitative precipitation estimation (QPE).
  • Analyze real-time and archival case studies to extract information on cloud cover, precipitation intensity, and atmospheric profiles.
  • Develop and test new algorithms for satellite data processing in collaboration with data scientists and meteorologists.
  • Support precipitation enhancement operations by identifying suitable cloud targets and assessing seeding effectiveness.
  • Contribute to scientific research and publish findings in peer-reviewed journals.

Requirements

  • Master's degree or Ph.D. in meteorology, atmospheric science, remote sensing, or a related field.
  • Deep understanding of satellite remote sensing principles and meteorological processes.
  • Experience with large dataset analysis, including distributed computing, database management, and GPU acceleration.
  • Proficiency in AWS cloud computing for data source and instance management.
  • Expertise in Python, MATLAB, R, or C++.
  • Knowledge of machine learning and deep learning techniques for satellite applications.

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