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 pre-processing 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)
• 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.
Qualification: Master in Computer Science, Mathematics or Physics
Required Skills: Good background in web technologies, image processing, and rendering. Familiarity with Python and a stack of relevant Python libraries.
Conditions: The anticipated duration of the PhD is 3 years.
Application Deadline: March 31, 2019