IPE 03-21 Master Thesis or Internship Data-Intensive cloud infrastructure for future control systems

Control systems of large scientific experiments include multiple thousands sensors monitoring

operation of used instrumentation and properties of ongoing experiment. This information is crucial for

understanding of  measured data and it should be preserved for a long time after the active

experimentation is finished. Often proper manual data interpretation and quality assurance is difficult or

even impossible due to the tremendously increase of both number and size of datasets. This raises the

need for novel automatic or semi-automatic data analysis methods and tools. Recording and

organization of control data also present a few challenges due to increasing sampling rates and sheer

amount of stored data during experiment lifetime. 

The internship is integrated into the project aimed to design a novel cloud-based architecture for control systems of large-scale scientific experiments. The system will integrate the data recorded by various subsystems and make it available to users in a uniform, comprehensible, and easy-to-use fashion. Scientists at IPE are working with a wide range of advanced technologies to realize real-time monitoring, ensure uninterrupted recording of data, and provide sophisticated data analysis and visualization platform working with terabytes of historical data. We offer students a possibility to join the project and apply their skills to solving practical problems at intersection of science and IT technology. The successful candidate will contribute to the instrumentation for large-scale physical experiment like Karlsruhe Tritium Neutrino and future dark matter experiments. He will work with cutting-edge control, cloud, database, and web technologies. The specific topic will be agreed based on the applicant skills and interests during the interview.

Persönliche Qualifikation

• Basics of data analysis, mathematical statistics, etc.
• Good programming skills and prior experience with Python. Understanding of best practices in code organization and maintenance.
• Practical experience in one of the following: relational, nosql, or time-series databases; cloud management and orchestration with a focus on Kubernetes-based systems; control systems (in particular EPICS platform); web technologies
• Linux system administration and understanding the basics of system engineering is a plus

Organisationseinheit

Contract duration        

Expected duration: 6-months

Contact person                    

Suren Chilingaryan suren.chilingaryan@kit.edu

Phone: +49 721 / 608 26579

Jalal Mostafa jalal.mostafa@kit.edu Phone: +49 721 / 608 25630