KI:STE
AI Strategy for Earth system data

The KISTE project is looking to recruit doctoral researchers, postdocs and software engineers!

Develop AI methods for environmental data

Build an environmental
AI e-learning platform

Build an environmental
AI platform

About KI:STE

The KISTE project aims to exploit recent developments in artificial intelligence – especially deep learning methods – for sound environmental data analysis. The scientific goal is the implementation of current AI approaches for spatiotemporal variable pattern recognition and pattern analysis in environmental data in the subject areas clouds, snow/ice, water, air quality and vegetation within the framework of five dissertations. In addition, a technical platform will be created to make powerful AI applications on environmental data available in a portable way. An online AI learning platform will be established with Interfaces to this AI platform. This e-learning offer is aimed at the location-independent education of young scientists and other interested parties. It will use the concepts and methods developed in the five research fields as teaching material.

Work packages

Clouds

Improved predictability of the availability of solar energy through the analysis of spatial-temporal patterns of cloud parameters based on geostationary satellite observations

Snow / Ice

Improved prediction of melting processes in glacial ice by combining meteorological environmental parameters with measurements from in-situ sensors and process simulations, as well as exemplary analysis of the consequences for hydrological runoff models

Water

Improve predictions of droughts or floods by examining extreme hydrometeorological events from the combination of simulation and observation data

Air quality

Air quality: Development of a methodology for seasonal forecasts of extreme values of air pollution by examining the influence of plant health on air quality

Vegetation

Development of multi-task methods to predict several parameters of the plant condition simultaneously, especially with regard to extreme events.

KI:STE is HIRING!

The KISTE project is looking to recruit doctorate researcher, postdocs and software engineers.

Melting and Freezing in hybrdi models
PhD Position
Aachen University
RWTH
Cloud variability and solar energy
PhD Position
Universität
zu Köln
Unsupervised learning for biogenic emissions

Get in touch

Phone: + +49 2461 61 6402
Email: TBD
Forschungszentrum Jülich GmbH
Institute for Advanced Simulation (IAS) Jülich Supercomputing Centre (JSC)
Wilhelm-Johnen-Straße 52425 Jülich Germany
MON-FRI 08:00 - 17:00

Get Started

  • Resources
  • Tutorials
  • Examples
  • Docs

About

  • Resources
  • Tutorials
  • Examples
  • Docs

Features

  • Overview
  • Design
  • Code
  • Collaborate

Follow Us

  • Twitter
  • GitLab

KI:STE