Deep Learning for Environmental Science

3 days ago


Århus, Århus Kommune, Denmark The University Of Illinois Full time
Deep Learning for Environmental Science

This PhD project is part of 'Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity', funded by Villum Fonden. We aim to develop innovative mathematical models to improve our projections and understanding of non-linear aquatic ecosystem processes using deep learning algorithms.

Key responsibilities include:

  • Supporting the deployment of high-frequency loggers at Lake Ravn, Denmark, and conducting regular microstructure profiler measurements of turbulent mixing and fluorescence.
  • Curating high-frequency data from global sources to increase data volumes and quality-controlling the data to create a database of time-dynamic, depth-discrete water quality measurements.
  • Applying exploratory machine learning techniques to discover underlying relationships in the data and comparing these projections to state-of-the-art vertical 1D aquatic ecosystem models.

The successful candidate will have the opportunity to work closely with colleagues at The University Of Illinois as well as active collaborations with scientific institutions in North America and Europe.



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