Position number IPE 02-20
IPE 02-20 Internship or Master Thesis: Remote visualization of large scientific data archives
Recent improvements in detector instrumentation provide unprecedented details to researchers. At the same time the data rates are continuously increasing. It is a challenge to quickly and efficiently extract knowledge from the waste volumes of data and present it to users in easy to interpret visual form. Advanced visualization techniques are essential for collaboration in the international scientific community and to realize useful raw data catalogs. This is equally true for the high energy physics at LHC, planned future lepton and neutrino detectors, as well as for experiments at high-intensity light-sources such as the EU-XFEL or PETRA-III.
The master thesis will be performed within a project that aims to develop a cloud-based infrastructure enabling remote data analysis and visualization. You are expected to evaluate state-of-the-art technologies and build a novel visualization framework on top of the selected libraries and tools. The basic responsibilities include data organization, image pre-processing, and web-development. The visualization framework is expected to show different aspects of the stored data, e.g. visualization of raw, pre-processed, and segmented data; multi-modal data visualization; visualization of time-resolved (4D) tomographic volumes. Optimal data organization should be proposed to enable fast visualization of a region of interest. Existing traditional and ML-based methods should reviewed and optimal solution selected in order to prepare data for visualization. This includes correction of acquisition and reconstruction artifacts, optimization of initial data view, noise reduction, etc. Further, the intelligent data reduction techniques are required. It is necessary to extract the reduced datasets suitable for visualization on the client hardware, but as much as possible representative of the original dataset.
Required Skills: The student is expected to know modern web technologies well and to be familiar with basics of image processing. Familiarity with Python and a stack of relevant Python libraries is also required. Experience with NodeJS framework is a plus. Prior experience with OpenShift or Kubernetes platforms is a plus as well.
OrganisationseinheitInstitut für Prozessdatenverarbeitung und Elektronik (IPE)
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Suren Chilingaryan: firstname.lastname@example.org, Phone: +49 721 / 608 26579
Areas Kopmann: email@example.com, Phone: +49 721 / 608 24910
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Personalentwicklung und Berufliche Ausbildung (PEBA) - Abteilung Berufliche Ausbildung
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