University of Geneva, Switzerland - PhD Position in Heliophysics and Machine Learning

The University of Geneva invites applications for a PhD student position in Heliophysics and Machine Learning. The student will work in the interdisciplinary PRIMA group “Understanding Solar and Stellar Flares with Machine Learning” funded by the Swiss National Science Foundation. Solar and stellar flares are highly energetic eruptions on the Sun, which however are neither fully understood, nor can they be well predicted. This project has the goal of developing, implementing, and using methods to analyze large (Terabyte) astronomy data sets to better understand the physics of flares.

The PhD student will be employed at the computer science department of the University of Geneva, under the supervision of the project leader, Dr. Lucia Kleint and the head of the stochastic information processing group Prof. Dr. Slava Voloshynovskiy. Founded in 1559, the University of Geneva is the third largest university in Switzerland by number of students, and ranked second in Switzerland according to the Shanghai Ranking of World Universities 2019. The PhD student will learn about solar flares, spectroscopy and spectropolarimetry, data analysis, machine learning, and programming, and will be able to interact both with scientists at the computer science department and in astronomy. The length of a PhD is typically 3-4 years. Support for conferences and collaborations, as well as potential telescope observing is available. 

PROFILE:

  • We are looking for highly motivated candidates with (or obtaining soon) a MSc in physics or astronomy.
  • Knowledge of programming and/or handling of large data would be beneficial.
  • At least basic knowledge of astronomy is required.
  • Strong verbal and written communication skills in English.
  • Strong analytical abilities and problem solving/troubleshooting skills.

The selection of candidates will start after September 10, 2020 and continue until the position is filled. The starting date is negotiable, preferably around January 1, 2021. Applications shall include a CV, a 1-page statement explaining the motivation for the application, a copy of BSc and MSc transcripts of courses and grades (scans of official transcripts are sufficient) and if available, a link to the Master’s thesis. Two letters of recommendation shall be sent before the application deadline directly by the referees to Lucia Kleint.

For more information about the project and to submit your application please contact:

  • Dr. Lucia Kleint (lucia.kleint@fhnw.ch)