PhD position: Intelligent data visualization and mining techniques
Scientific experiments are currently undergoing a drastic change. New detectors systems offer ever increasing spatial and temporal resolution. Automation enables high throughput of samples. As result the amount acquired data is exploding and puts large challenges to research facilities and scientists. In order to tackle data-driven science, the standard data acquisition schemes need to be extended by advanced visualization and data mining techniques. International collaboration require an intelligent remote data access as the size of the data makes arbitrary data copies slow or even impossible. However, with a size of typical dataset in a gigabyte range, even data visualization becomes a challenging task. Computationally intensive preprocessing is required to extract domain specific information and detect relations between datasets.
You are going to develop a novel data visualization framework for large archives of tomographic volumes. With a 3 stage visualization workflow combining pre-processing as well as a server- and client-side rendering we want to cater a high-quality visualization to clients ranging from handhelds to large visualization stations and keeping load on the server-side infrastructure to a minimum. As a pilot project, an archive of samples produced at synchrotron facilities for research in developmental biology is considered. The practical aspects include:
- Multiple visualization modes: visualization of raw, pre-processed, and segmented data; multi-modal data visualization; Visualization of uncertainty in segmentation; visualization of time-resolved (4D) tomographic volumes.
- Optimal data organization for a region of interest visualization.
- Collaborative analysis tools like support for annotations and multi host visualization.
- Various pre-processing filters to enhance the quality of data, to remove holders/containers, and to rotate the object to an optimal initial view.
- Efficient data reduction techniques to extract the reduced datasets suitable for visualization on the client hardware and still representing the complete dataset.
- Advanced rendering techniques: multi-resolution rendering, progressive rendering, and volume compression. Server- and client-side rendering should be combined for optimal performance.
- Optimization for non-standard visualization systems and integration with virtual reality environments.
- Running in a cloud environment to ensure high availability and easy scalability.
You have a university degree (diploma (Uni) / Master) in the field of computer Science, mathematics or physics. You also have a good background in web technologies, image processing and rendering. Familiarity with Python and a stack of relevant Python libraries
Institute for Data Processing and Electronics (IPE)
limited to 3 years
Application up to
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For further information, please contact Mr. Chilingaryan, e-mail: email@example.com / phone: +49 721/608 26579 or Mr. Kopmann, e-mail: firstname.lastname@example.org / phone: +49 721/608 24910.
Please apply online using the button below for this vacancy number 24-2018-IPE.
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