The graduate school aims at the education and training of the next generation of data scientists in close contact to data generation. The application context is given by research fields characterized by the generation of big and heterogeneous data sets which require interpretation in the context of mathematical models. The involvement in projects from the following scientific fields will ensure focusing on realistic practical problems and the training of inter-disciplinary analysis and communication skills:
The PIs and involved partner institutions have strong expertise in these fields, operate extensive state of the art infra-structures generating large amounts of heterogeneous data and have the (super-)computing facilities to integrate big multi-source data sets with different kinds of models.
As diverse as the chosen disciplines might appear at first sight, they have much in common from a methodological point of view . In each of these fields extremely complex heterogeneous systems are investigated, whose behavior is neither fully understood nor can be modeled entirely from first principles. Thus, all fields are characterized by:
- A high system complexity making modular, model-based and/or multi-scale approaches mandatory
- High-performance and high-throughput measurement instruments requiring sophisticated algorithms for raw data processing
- A high degree of data heterogeneity due to simultaneous monitoring of different physical, chemical or econom-ical parameters
- The need for advanced concepts to effectively integrate data from different sources including extended data-bases and ab initio computed data
- The requirement for artificial intelligence methods and novel modeling approaches which simultaneously can deal with missing knowledge and uncertainty, but also with first-principles knowledge
- A strong demand for application-specific intuitive tools for data visualization, consistent interpretation, prediction and/or design
By dealing with the respective domain-specific problems at this level of abstraction, it will be possible to train the doctoral candidates in a highly interdisciplinary environment and to achieve a multi-disciplinary cross-fertilization by the transfer of ideas, methods and tools between the disciplines.