Work group:

Institute for Data Processing and Electronics (IPE)

Area of research:

PHD Thesis

Job description:

Complex and distributed detector and control systems are required for modern scientific experiments. The instrumentation integrates custom and commercial components from various sources and generates everincreasing amounts of data. A variety of different formats, underlying storage engines, and data workflows are used. 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. Information on operation and scientific meaning needs to be extracted from the data stream and provided to the users in visual and easy to interpret form.

You are going to develop a novel platform for handling data acquisition and data management tasks of mid-range scientific experiments. As pilot projects, we want to:

Refurbish the data acquisition system of Katrin Tritium Neutrino experimentDevelop a novel data management platform for tomographic data. We plan to build tools to integrate the data recorded by different subsystems and made it available to users in uniform, comprehensible, and easy-to-use fashion. We aim at systems which can be catered by a relatively small clusters built from off-the-shelf components. Particularly, we want toRun software on a virtualized platform which can easily hold components running on different operating systems and using a different set of technologiesUse novel techniques to distribute massive amounts of data and ensure its consistencySelect an optimal database engine to store ever increasing amount of data and exploit an intelligent caching to speed up queriesIntegrate automatic and user-generated metadata fully describing how the data was obtained and also relations between different datasetsInclude automated data quality monitoring using provided data-inspection modules, but also trying to find data anomalies using deep learning techniques.Foresee workflows for collaborative data analysisEnsure data traceability and provide internal mechanism to reference the dataRely on cloud technologies to ensure scalability and high-availability

You have a university degree (diploma (Uni) / Master) in the field of computer Science, mathematics or physics. You also have expert knowledge of software and system engineering and a good background in cloud and database technologies. Familiarity with Python and a stack of relevant Python libraries.

Contract duration

limited to 3 years

Application up to

31.03.2019

Contact person in line-management

For further information, please contact Mr. Chilingaryan, e-mail: suren.chilingaryan@kit.edu / phone: +49 721/608 26579 or Mr. Kopmann, e-mail: andreas.kopmann@kit.edu / phone: +49 721/608 24910.

PhD position: Intelligent cloud-based data acquisition platform

Karlsruhe Institute of Technology (KIT) - KIT - Helmholtz Association

Karlsruhe, Germany