For more information, please follow this link: https://boards.greenhouse.io/sofarocean/jobs/5010468003

Salary– DOE

Sofar is on a mission to connect the world’s oceans. We design, build, and deploy the largest privately owned network of marine weather sensors to power the world’s best marine weather forecasts. Our data helps our customers to increase efficiency and reduce emissions, delivering tools to governments and other stakeholders to understand impacts of climate change on extreme weather and ocean health. We live on Planet Ocean, and our goal is to create the ocean intelligence needed to ensure a sustainable future.

The Role

Sofar designs, builds and deploys ocean sensing hardware at a global scale. With more than 600 units in the water (and still growing rapidly) we have unique observational knowledge of the state of ocean weather – which we use (through data assimilation and machine learning) to drive the most skillful global wave forecast in the world. We are now ready to take the next step and create a best in class surface coupled atmosphere-wave-ocean forecasting system, and are looking for an ambitious scientist to take up this challenge. As part of the ocean science team this role will collaborate with other scientists and software engineers to create a state of the art forecasting system that – through coupled data assimilation – produces best in class surface wind forecasts over the world’s oceans.

Responsibilities:

  • Collaborate with an interdisciplinary team of scientists and software engineers to build a world class forecast system leveraging Sofar’s unique global observing system
  • Bring your unique expertise in machine learning to complement our ocean science team.
  • Develop machine learning based pre-processing methods for quality control and bias correction inputs to the operational forecast system.
  • Develop machine learning based post-processing methods to enhance skill of operational forecasts of ocean currents, waves, and winds over the ocean.
  • Implement customized machine learning solutions to improve/enhance numerical modeling of waves, atmosphere, and ocean dynamics, all implemented on the cloud.
  • Coordinate with wave, ocean, and atmosphere scientists/modelers to identify potential machine learning enhancements to the coupled modeling interface.
  • Develop machine learning methods to tune the conventional atmospheric, wave, and ocean models to improve forecast skill of coupled atmosphere-wave-ocean dynamics.
  • Develop data-driven surrogate models as low-cost alternatives to provide skillful forecasts.

Minimum Job Qualifications

  • Experience with large-scale machine learning implementations for scientific applications
  • Working knowledge of Earth science and Earth system modeling
  • Experience working with large datasets produced by numerical atmospheric models such as FV3-GFS, WRF, GEOS AGCM, IFS, and ocean models such as MOM6, HYCOM, ROMS, or NEMO.
  • Experience with high level programming languages such as Python, Fortran, C++.
  • Excellent communication skills, and you excel working in a cross-functional team.

Bonus Points

  • In depth knowledge of processes involved in air-sea interaction
  • In depth understanding of the marine atmospheric boundary layer and ocean mixed layer
  • Experience coupling atmosphere, wave, and/or ocean model components