Funding objectives

The objective of this funding guideline is to advance knowledge in biomedical research through innovative in silico approaches and to improve the prevention, diagnosis, and treatment of diseases. At the same time, the aim is to attract promising talents from the quantitative, computer-based sciences who are about to complete or have recently completed their doctorates to pursue an academic career in the interdisciplinary field of eHealth. This will enable them to further develop their methodological skills and acquire relevant key qualifications such as project management, thereby creating the conditions for them to subsequently apply for further third-party funding and/or a junior research group.

What is funded?

Funding is available for individual projects at universities led by scientists who are about to complete their doctorates or have recently completed them, or who have already worked on an initial research project as postdocs. The program is aimed in particular at interdisciplinary researchers with a background in computer science and mathematics and initial research experience in data-driven health research. The Postdoc Starting Grants are intended to enable them to implement their first independent research program. There is no age limit for applicants, but the doctorate must have been completed no more than four years prior to the date of application, plus any periods of child-rearing.

The projects should further advance the development of new in silico approaches for health research by addressing a clinically relevant research question and further developing suitable digital technologies. This includes projects that, based on existing data sets,

• Contribute to improving the quality, standardization, linking, and integration of biomedical data, particularly health data, and promote the exchange and use of data from healthcare, clinical, and biomedical research across institutional and geographical boundaries.
• developing evidence-based decision support systems using a mix of methods from, for example, medical informatics, epidemiology, statistics, and biometrics;
• creating innovative IT prerequisites for optimizing personalized treatment approaches;
• address clinically relevant challenges in biomedical data analysis using innovative, AI-based computer-assisted methods, or further develop data-driven systems medicine research approaches towards concrete applications for diagnosis, therapy, and prevention;
• significantly improve the current state of the art by developing new methods and software tools for mathematical modeling and simulation of complex biomedical systems, pathophysiological mechanisms, or the spread of serious infectious diseases.

Futher information can be found here.