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IPE 02-20 Internship or Master Thesis: Remote visualization of large scientific data archives

Karlsruhe Institute of Technology (KIT) – The Research University in the Helmholtz Association creates and imparts knowledge for the society and the environment. It is our goal to make significant contributions to mastering the global challenges of mankind in the fields of energy, mobility, and information. For this, about 9300 employees of KIT cooperate in a broad range of disciplines in research, academic education, and innovation.

Organizational unit

Institute for Data Processing and Electronics (IPE)

Job description

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.

Starting date

on appointment

Personal qualification

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.

Contract Duration

according the study regulations

Contact person in line-management

Suren Chilingaryan: suren.chilingaryan@kit.edu, Phone: +49 721 / 608 26579

Areas Kopmann: andreas.kopmann@kit.edu, Phone: +49 721 / 608 24910


If qualified, severely disabled persons will be preferred.

Please apply online using the button below for this vacancy number IPE 02-20.

Personnel Support is provided by

Ms Schaber
Telefon: +49 721 608-25184,

Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany