The near-Earth space environment is hazardous and poses a significant risk for satellites and humans in space. Currently, there are hundreds of operational commercial satellites with a revenue stream of tens of billions of dollars per year. There are also a number of other satellites that assist in navigation, weather prediction, and telecommunication. Frequent satellite failures caused by space weather have fueled a surge in interest with specification and prediction of space weather during the last decade. 

Our section "Space Physics and Space Weather" is working on understanding the dynamical evolution of the hazardous space radiation environment and developing tools for specification and prediction of the adverse effects of space environment using models and artificial intelligence. We study the near Earth environment and focus on understanding fundamental processes responsible for the evolution of space radiation. Our research will help safely design and operate satellites and maintain ground networks. Our section has developed a number of ML-based models focusing on the analysis of solar images, simulations of Earth' plasmasphere, magnetosphere, the geomagnetic index Kp, and the radiation belts. The responsibilities of the research associate will include the development of new models and application, as well as the maintenance of existing machine learning codes. The Research Associate will be also involved in fusion of the ML models with physics-based models. 

This position is suitable for both PD (Postdoc) and PhD career stages.

Your responsibilities:

• Develop machine-learning pipelines
• Develop codes-based machine-learning algorithms
• Develop real-time visualizations
• Maintain data and code repositories
• Document and test existing codes

Your qualifications:

• For PhD's: MSc in physics, computer sciences, maths or related natural sciences OR for PD's: PhD in physics, computer sciences, maths or related natural sciences
• Experience/knowledge in artificial-intelligence/machine-learning algorithms
• Experience/knowledge in data-science concepts
• Ability to work within independently and in a team
• Attention to detail and strong organizational skills
• Excellent interpersonal and communication skills
• Good programming skills in Python/Matlab

A plus:

• Ability to work with large datasets
• Knowledge of Unix operating systems
• Knowledge of git and docker