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Accelerating Research Through Individualized Funding Intelligence

Project description

Assistance tool to help researchers find funding

Access to funding is the foundation of virtually every research project, and new methods to help researchers find relevant grants can provide significant benefits for the scientific community. The EU-funded ARTIFI project is developing a machine learning engine capable of actively learning from interactions with researchers and helping them find funding opportunities they would not typically have thought of on their own. Developed by a Dutch SME, this project leads to the creation of a new service that helps researchers identify the best funding opportunity, and build important skills and use cases for the implementation of new machine learning approaches such as active learning.

Objective

Researchers are under ever increasing pressure to compete for research funding. More and more time is spent on writing lengthy proposals with dwindling success rates. IDfuse is a Dutch SME that wants to help researchers identify specialised, less well known funding opportunities. In 2016, IDfuse has launched impacter.eu a successful online tool that runs automated analyses on drafted grant applications, allowing academic researchers to strengthen their grant proposals. IDfuse envisions to go a step further in its service by providing researchers with alternative funding opportunities based on the reviewed proposal and the scientific profile of the researcher, thereby accelerating research through individualized funding intelligence (ARTIFI). The envisioned product should essentially consist of a machine learning engine that is able to actively learn from interacting with the individual researcher. Moreover, in order to be able to present researchers with opportunities that go beyond what they would typically think of, IDfuse envisions that the engine should be able to expand the researcher profile with insights that can be extracted from open source research materials, including abstracts and open access scientific publications. In order to fully explore the development of such learning algorithm, expertise in the field of artificial intelligence and active machine learning is required. IDfuse currently does not have this expertise in its R&D team and it has not been able to recruit an individual with such expertise. This is mostly due to the low availability of suitable candidates and the high salary packages that larger companies are able to offer them. EC funding for an Innovation Associate will enable IDfuse to offer prospective candidates a competitive employment contract that additionally includes a tailored training programme. With this, IDfuse is convinced that it will be able to attract a candidate with the expertise to successfully complete the project.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

CSA - Coordination and support action

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-INNOSUP-2018-2020

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Coordinator

IDFUSE BV
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 101 690,00
Address
EUROPALAAN 100
3526 KS Utrecht
Netherlands

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
West-Nederland Utrecht Utrecht
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data
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