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Data Science Investigator – Distinguished 2023

Call opens
20 March 2023
Call closes
15 August 2023 2:00pm (CEST)
Announcement of results
December 2023
Recurring calls
Spring 2024
Application guidelines

Purpose

The Data Science Investigator Programme is composed of three calls – ‘Emerging’, ‘Ascending’, and ‘Distinguished’. Each of these calls targets principal investigators at a specific career stage.

The ‘Data Science Distinguished Investigator’ is a grant for outstanding professors who have demonstrated the ability to execute and lead research at the highest international level. The Distinguished Investigator grant is for Professors of all ages.  Key is that the project is novel, excellent, has potential high impact and is of a character that justifies a five-year grant of this magnitude.

The purpose of the ‘Data Science Distinguished Investigator’ grant is to support outstanding professors with novel research ideas within the field of data science and computational science that have a potential for high impact, with immediate or potential future applications within areas of relevance to NNF’s Strategy.

The intended impact is to strengthen the quality and size of the Danish academic environment for data science research and education, which in turn will allow for an increased and improved output of new candidates skilled in data science, to meet the strong demand for such competencies from all sectors of society.

Assistant and Associate Professors are encouraged to consider the ‘Ascending’ and ‘Emerging’ calls.

About the grants

Amount

Up to DKK 11 million per grant

Location

Denmark, International recruitment to Denmark

Career stage

Research Leaders (Established/Prof.)

Research area focus

Data Science

Committee

Committee for Data Science

Contact

For grant inquiries
Anna Chailyan
Senior Scientific Manager
[email protected]

Areas of support

Research proposals which are eligible for funding should address:

  1. Development of new algorithms, methods and technologies within data science, artificial intelligence (incl. machine learning and deep learning), statistics, bioinformatics, and other computational sciences (incl. mathematical modelling, simulations, etc.)

and/or

  1. Applications of data science and computational sciences to areas of relevance to NNF’s strategy, including life science, biomedical- and health science, public- and global health, infectious disease, sustainability, green transition, agriculture, as well as natural and technical sciences.

For projects mainly concerned with methods development, it is important that the applicants argue convincingly for potential application and impact within NNF’s scientific focus areas (Health, Sustainability, Life Science Ecosystem). Vice versa, projects that have their primary focus on the application side must describe and explain the novelty and impact of their data- and computational science approach, be it development of novel methods or novel applications of existing methods.

In general, projects without potential applications within NNF’s scientific focus areas and/or projects with no novelty in terms of development or application of computational methods will not be funded in this programme. Projects where the primary focus is on financial or insurance, fraud detection, advertisement, commercial analysis, telecommunication, mass surveillance, defense, cyber security, gaming, etc., are considered outside of scope and will generally not be considered for funding.

Eligibility 

The ‘Data Science Distinguished Investigator’ grant is aimed at supporting outstanding Professors who have demonstrated the ability to execute and lead research at the highest international level.

  • Applicants must be Professors (of all ages), with their own research group and individual research profile.
  • Applicants are expected to have a documented track record of high-impact scientific contributions to the field of data science, which will typically include authorship of research papers in high-impact journals, contributions to key conferences in the field, etc.
  • The proposed research within data science should be novel and have a potential for high impact in the field.
  • Applicants must be ‘full-time’ researchers (teaching obligations included) who, during the project, are primarily employed, and have their primary research group, in Denmark. The project applied for must be anchored at a university or other non-profit research institution, which will be considered the administrating institution of the grant. The applicant must be the principal investigator of the proposed project and should perform independent research.
  • It is a requirement that the applicant contributes to educational or training activities within data science or computational science at their host institution and/or at other institutions in Denmark.

Funding 

Each grant can be up to DKK 11 million over a period of up to five years.

The total annual grant budget for the 2023 Data Science Investigators Programme is up to DKK 75 million, across all three career levels.

Application to other NNF programmes

It is possible for researchers to apply for the Data Science Collaborative Research Programme open call (as either a main or co-applicant) in addition to the Investigator Grant but the following restrictions apply:

  • The applicant must clearly indicate which other submitted proposals include her/him as a main or co-applicant.
  • The applications should not be contingent on each other.
  • Any overlap in the project description should be indicated clearly.
  • An applicant can only be awarded a single Data Science open competition grant (e.g., either the Collaborative Research Programme or Investigator grant) from NNF within a given year.

Application process 

The application must be completed and submitted using the foundation’s application system NORMA that can be accessed via the link “Apply now”.  

Please read “Information and Guidelines for Applicants” carefully before initiating the application process. The Guidelines provide essential information on the call and the application content and process. 

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