IPE 04-2023 Master Thesis or Internship: Development of a Cloud-based Scientific Visualization Framework for large-scale experiment data
- Institute for Data Processing and Electronics (IPE)
Scientific visualization enables researchers to transform complex and abstract data into visually comprehensible representations. Through various visualization techniques, scientists can identify patterns, trends, and anomalies that might be difficult to discern in raw data alone. This process helps in acquiring a deeper understanding of the underlying phenomena and can lead to new insights and hypotheses.
In this project, you will be part of an overarching project that is designing and implementing a cloud-based scientific visualization framework for large scale experiment data. The framework must process and visualize data using cloud resources. Notably, the visualization modules can be integrated into the existing data monitoring framework of the KATRIN experiment (Karlsruhe Tritium Neutrino Experiment). The primary goal of this project is to leverage the scalability and computational power of the cloud to enable real-time or near-real-time interactive visualization of large datasets, overcoming the limitations of traditional local rendering techniques. You will be involved in part of the key tasks shown below:
- Literature Review: Conduct an in-depth literature review to understand existing cloud-based rendering techniques, volume visualization algorithms, and cloud computing platforms. Identify the most suitable approaches to form the foundation of the new framework.
- System Design: Based on the insights gained from the literature review, design a robust and scalable cloud-based scientific visualization framework. Consider factors like data storage, data transfer, load balancing, and resource management.
- Integration of Rendering Algorithms: Incorporate state-of-the-art visualization algorithms into the framework to enable high-quality visualizations.
- Performance Optimization: Investigate and apply optimization techniques to ensure efficient rendering performance on cloud resources.
Testing and Evaluation: Thoroughly test the developed framework using various scientific datasets and evaluate its performance in terms of rendering speed, image quality, and scalability.
- Familiarity with programming languages such as Python or C++
- Basic knowledge of computer graphics and visualization techniques
- Proficiency in cloud computing concepts and platforms (e.g., AWS, Azure, or Google Cloud) is advantageous but not mandatory
Carrasco Sanchez, Raquel
Tel: +49 721 608-42016