PhD at the intersection of Machine Learning and Earth Observation

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Deadline 1 August 2021

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Organisation: Royal Belgian Institute for Space Aeronomy

The Royal Belgian Institute for Space Aeronomy (BIRA-IASB) is opening a PhD position for a 4-year study to join the UV-visible observation team.

They are looking for an outstanding, highly motivated student with an MSc in Computer Science, Applied Mathematics, Artificial Intelligence, Physics or other relevant field to work in the intersection of Machine Learning and Atmospheric Sciences.

Research topic

Recent satellite missions addressing the global monitoring of the atmospheric composition show increased spatial resolution allowing, for the first time, to map the abundance of air pollutants at urban scales. In particular, the Sentinel-5 Precursor mission launched in support of the European Copernicus program provides daily maps of several gases of interest for air quality and climate studies. The job will focus on using artificial intelligence (AI) to describe the physics to relate remotely sensed atmospheric data with ground observations from in-situ networks using a number of ancillary variables to better constrain this relationship. More specifically, the study will consist in setting up and training a Machine Learning algorithm to relate Sentinel-5p and future Sentinel-5 and Sentinel-4 tropospheric column data of nitrogen dioxide (and possibly other gases) to near-surface concentrations by synergistic exploration of the Sentinel data, observations from in-situ air quality networks and various meteorological and land use parameters. The PhD candidate will work in an international environment, implying the dissemination of results in scientific publications and presentations at international conferences and workshops.

Required competences
  • MSc degree in a relevant field for this project (e.g. Computer Science, Applied Mathematics, Artificial Intelligence, Physics)
  • Expertise in Machine Learning and preferably its application on earth observation data
  • Interest in remote sensing of the atmospheric composition and in atmospheric physics and chemistry
  • Good knowledge of scientific programming languages and data visualization tools
  • A critical and organized sense for data analysis
  • Strong communication skills, including a good proficiency in English (oral and written)
  • Ability to work autonomously and in a team
  • Good level of flexibility, ready to travel occasionall
Planned start date

September 1, 202

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