Stage Event: Internship Computing Group
Geplaatst op: 31-10-2019 | Solliciteren voor: 02-01-2020
ASTRON is the Netherlands Institute for Radio Astronomy. Its mission is to make discoveries in radio astronomy happen, via the development of novel and innovative technologies, the operation of world-class radio astronomy facilities, and the pursuit of fundamental astronomical research.
The ASTRON Research & Development division is responsible for the technical program. The program focuses on innovative instruments for existing telescopes as well as on developing technologies for future observing facilities.
Description of the ASTRON assignment
In modern radio telescopes such as LOFAR, System Health Management (SHM) systems are crucial for detection and identifying system errors. Due to the increasing scale and complexity of the systems involved, this is becoming more and more difficult. Machine learning approaches (AI) for clustering error features are currently being investigated, but these lack a good training set, example spectrograms with known features, a so-called ground truth.
The aim of this assignment is to create this ground truth, a dictionary of example spectrograms in which system errors are associated with typical patterns in the spectrogram images.
The student is expected to:
- write Python code to read LOFAR spectrogram data,
- to select and apply image filters to isolate image features,
- to relate (with support from ASTRON) features to system errors, and
- to create an initial system error dictionary.
For more information please contact M.Sc. Walter Jansen, Group Manager Computing, email@example.com or dr. ir. Albert Jan Boonstra, Programme Manager Technical Research, firstname.lastname@example.org and email@example.com
You can send your CV and motivation to firstname.lastname@example.org.