The information infrastructure project aims at removing barriers that impede an efficient collaboration between researchers in the CRC and between CRC researchers and external collaboration partners. The CRC addresses several variations of data-assimilation and model-building problems. Experimental evaluations involve data from different application areas, and algorithms in a multitude of programming languages. Researchers in the CRC publish in different fields, and therefore even published ideas and results are not in all cases easily accessible by other researchers. This project's goals therefore are to facilitate the sharing of data, algorithms, and results, the reproduction results from experimental studies, and the development of shared software tools. In order to achieve these goals, the project provides information-technology infrastructure in the form of data storage, wikis, and software repositories, and defines and implements processes and certification procedures for the documentation of data sets, algorithms, and software interfaces. The project will facilitate exploitation of the computing infrastructure of the HLRN supercomputing center within the CRC by developing a course program as part of the integrated graduate school. The project will establish a data-management and certification board and host a working group on application programming interface (API) specification and efficient implementation of algorithms for doctoral and postdoctoral researchers of the CRC.
The postdoctoral or doctoral researcher will work at the Department of Informatics Computational Science at the University of Potsdam and will design the CRC's collaboration infrastructure (such as wikis and code repositories), design processes for software-engineering, quality assurance, and research datamanagement, establish these standards within the CRC, and consult and collaborate with researchers in the CRC. Candidates should have adegree in computer science and should have a strong background in software engineering, collaboration tools, research data management as well as interest and background knowledge in data science.