Purpose
The Data Science Investigator Programme targets principal investigators at three different career stages – Emerging, Ascending and Distinguished.
The purpose of the ‘Data Science Distinguished Investigator’ grant is to support outstanding professors who have demonstrated the ability to execute and lead research at the highest international level, with ambitious projects within data science that:
- lead to new or improved core data science algorithms, methods and technologies,
and/or
- explore and expand data science applications to real-world scientific problems within the scope of the NNF Data Science Initiative (see Areas of Support).
The Distinguished Investigator grant is intended for full professors (MSO included) of all ages. Less senior researchers are encouraged to consider the ‘Ascending’ and ‘Emerging’ profiles.
Part of the NNF Data Science Initiative
The Data Science Investigator Programme is one of 4 pillars of the NNF Data Science Initiative, through which the Foundation aims to strengthen the Danish academic research environment within data science and artificial intelligence, as well as support the education and training of the next generation of data scientists.
Other calls in open competition include the Data Science Research Infrastructure Programme and the Data Science Collaborative Research Programme. In addition, an academy for data science, with the purpose of establishing a network and distributing fellowships, is under design.
Areas of support
The research proposal must be linked to ongoing research and be within the scope of the NNF Data Science Initiative:
- Development of new algorithms, methods and technologies within data science, artificial intelligence (incl. machine learning and deep learning), data engineering, data mining, statistics, applied math, computer science, big data analytics, etc.
- Applications of data science (as defined above) within the Foundation’s core scientific areas: Biomedical and health science, life science and industrial applications promoting sustainability, as well as natural and technical sciences with potential application in biotechnology or biomedicine.
For projects mainly concerned with data science methods development, it is important that the applicants clearly show the relevance for potential future application and impact within life science, health science, or biotechnology. Vice versa, projects which have their primary focus on application of data science methods must describe and explain the novelty and impact of their data science approach, be it development of novel methods or novel applications of existing methods.
Eligibility
- Applicants must be Full Professors (MSO included).
- The applicant must be an active data science researcher who, during the project, will conduct and direct independent research within the field of data science.
- The project must be anchored at a university or other non-profit research institution in Denmark. Applicants must, during the project, be employed in Denmark and have their research group based in Denmark. Only minor and time-limited affiliations with institutions abroad are exceptionally allowed, as the grant is intended for ‘full-time’ researchers (teaching obligations included) based in Denmark.
- Recipients of this grant must contribute to data-science related courses in the pre-graduate teaching environment at their host institution and/or at other institutions in Denmark.
Funding
Each grant can be up to DKK 10 million, for a period of five years.
The total grant capital in 2022 is DKK 70 million across the Emerging, Ascending, and Distinguished Data Science Investigator Programmes.
Application to other NNF programmes
It is possible for researchers to apply (as either a main or co-applicant) for each of the different data science calls under the NNF Data Science Initiative, but:
- The applicant must indicate which other submitted proposals includes her/him as a main or co-applicant
- The applications should not be contingent on each other
- Any overlap in project description should be indicated clearly
