KIT Graduate School Computational and Data Science

The KIT Graduate School on Computational and Data Science (KCDS) strives for an uncompromised interdisciplinary education for all interested and prospective doctoral candidates who want to learn about and develop methods from both model-driven as well as data-driven computational science. We are convinced that the combination of data-based methods with system modeling approaches is the key to solving many of today’s and tomorrow's challenging problems. We therefore aim for 100 doctoral researchers within the school sustainably after the initial build-up phase and expect demand to grow. 

The initiative of a Computational and Data Science graduate school at the KIT Center MathSEE developed together with a BSc/MSc program of the same name. That program gained support from members of 9 KIT faculties, who all confirmed that this new and emerging interdisciplinary field requires a strong and well-connected network of several disciplines with a shared interest in problem solving. 

The initiative of a Computational and Data Science graduate school at the KIT Center MathSEE developed together with a BSc/MSc program of the same name. That program gained support from members of 9 KIT faculties, who all confirmed that this new and emerging interdisciplinary field requires a strong and well-connected network of several disciplines with a shared interest in problem solving. 

The main elements of the KCDS interdisciplinary training program are general competence, topical competence, cutting-edge knowledge, self-reliance, and networks/internationalization. Supervision is organized according to the MathSEE tandem principle of at least one advisor from the mathematical sciences, and at least one from the SEE disciplines. KCDS is committed to fostering diversity, equity and inclusion, and several measures have been designed to achieve this goal. 

The school will be organized through a central, dedicated office with a coordinator and a steering committee, which includes elected representatives of the doctoral researchers. Prospective students apply with a detailed letter of motivation, expressing interest and commitment to the content of the school. Quality of the graduate school is ensured by regular standardized surveys, statistics on performance indicators such as the number and quality of publications, participation in externally funded projects, innovations, and patents.