Description
The European XFEL generates extremely intense X-ray flashes used to explore the structure and dynamics of matter. In the Data Analysis team, we are researching and developing Machine Learning methods to automatize the analysis pipeline and optimize the beamtime taken by scientists when taking data. One of the projects under development aims to automatize the tuning of several parameters of a standard real-time analysis pipeline. The parameters need to be adapted to the data sample being collected during the experiment, so that the experimenter has fast feedback on the data quality and may adapt its experiment as needed. In this project, the master student shall study and compare the effects of the automatic tuning developed using different Machine Learning techniques on different scenarios and realistic datasets at the EuXFEL and possibly propose improvements on how the project may be improved based on it.
Special Qualifications:
The ideal candidate is expected to have experience on the following areas:
- Python
- Matplotlib
- Crystallography is an asset
- Machine Learning is an asset
| Field | A6: Theory and computing |
|---|---|
| DESY Place | Hamburg |
| DESY Division | other |
| DESY Group | EuXFEL Data Analysis |
