Karlsruhe Institute of Technology (KIT) – The University in the Helmholtz Association
In close partnership with society, KIT develops solutions for urgent challenges – from climate change, energy transition and sustainable use of natural resources to artificial intelligence, sovereignty and an aging population. As The University in the Helmholtz Association, KIT unites scientific excellence from insight to application-driven research under one roof – and is thus in a unique position to drive this transformation. As a University of Excellence, KIT offers its more than 10,000…
employees and 22,800 students outstanding opportunities to shape a sustainable and resilient future.
KIT – Science for Impact.
The new MorphoSphere project develops an advanced data management architecture for distributed, smart, and interactive storage and analysis of series of large 3D tomographic datasets. It integrates large-scale imaging facilities (PETRA III at DESY, KIT Light Source), high-performance computing and data facilities at DESY, KIT and the University of Heidelberg, and scientific communities to optimize data transfer, processing, and visualization, thereby accelerating scientific discovery across diverse research domains.
Your Tasks
Your responsibilities include in particular:
- Research lossless and lossy compression methods and analyze the image-processing pipelines employed by users
- Evaluate these methods on real datasets and develop a compression technique that achieve the highest possible compression ratio while preserving details essential for further processing
- Implement automated data-reduction workflows and integrate them with data-ingestion streams to storage facilities
- Develop a data access framework ensuring transparent access to compressed data for all user applications
- Leverage caching techniques to minimize data-loading latency for full datasets and subsets as required by user applications
- Publish a study on data compression and document the developed software
Your Profile
- A completed master's degree in computer science, mathematics, or related disciplines
- Strong understanding of system design principles with emphasis on performance, scalability, and architectural trade-offs
- ability to reason about and justify architectural decisions under real-world constraints;
- proficiency in programming, particularly with C and Python
- work experience with image-processing
- fluency with Linux
- experience with compression techniques, advanced databases, virtual file systems, HPC, and parallel programming with OpenCL/CUDA is a plus
