Immediate: we look for an excellent PhD candidate with a strong background in Machine Learning, Image Processing and/or Mathematics with strong programming skills. Please, see this page for mode details.
So far, flares are neither understood, nor can they be reliably predicted. The problem is that the patterns found in flares and in their temporal evolution are diverse and most complex and the data volume too large to analyze manually. A huge first step towards a better understanding of the underlying physics and the development of space weather forecasting is to systematically identify, collect, and characterize the different spatio-temporal patterns in solar physics data. Here, efficient big data analytics tools such as machine learning techniques are crucial.
We propose an interdisciplinary approach to set up, customize, and optimize analytics capabilities for big data applications in astronomy. The project team consists of astronomers, experts in machine learning and statistical image processing, and specialists in data management systems for Big Data astronomy projects. This interdisciplinary approach will allow for a drastic improvement in the level of science questions that can be addressed and will, in turn, lead to a quantum leap in the understanding of the physics of solar flares and the quality of space weather predictions.
In a first step, various existing state of the art machine-learning techniques for clustering, classification, and outlier detection will be applied. In a second step, we will develop algorithms customized to the science question to be addressed and to the statistics of the input data. For the processing and the analysis of the big data, we will setup a big data analytics system suited for optimizing machine learning algorithms to use cases in astronomy but also in other science domains.