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Training Europe’s Future Leaders in AI-driven Neuro-Oncology Diagnostics and Discovery

Objective

Neuro-oncology, at the crossroads of neuroscience and cancer care, plays a crucial role in treating some of the most aggressive and complex malignancies, such as glioblastomas. Despite recent advancements in research and technoloNeuro-oncology, at the crossroads of neuroscience and cancer care, plays a crucial role in treating some of the most aggressive and complex malignancies, such as glioblastomas. Despite recent advancements in research and technology, the field continues to face major hurdles in early detection, risk-adapted surgery, accurate tumour classification, and the development of effective personalized treatments, underscoring the urgent need for innovative solutions to improve patient outcomes. Current advancements in computer science, particularly in artificial intelligence (AI), are revolutionizing medicine and offer unprecedented opportunities to improve areas that have historically seen limited progress, such as neuro-oncological care. However, critical gaps remain between technical innovations and their clinical application. Moreover, physicians often lack the technical background to fully utilize AI, while computer science developments may not always address clinical realities. The FutureMIND Doctoral Network aims to bridge these divides and fill in the gap of holistic experts needed to advance AI-driven diagnostics and discovery in neuro-oncology. FutureMIND brings together leading researchers, clinicians and entrepreneurs from eight countries to train 14 Doctoral Candidates (DCs) across different disciplines and sectors. By the end of FutureMIND, the DCs will be equipped to advance digital medicine as a new clinical tool to tackle brain cancers, with foreseeable impact on thousands of patients. Importantly, FutureMIND will prepare a new generation of European experts capable of addressing future challenges and of preparing the EU to sovereignly lead the digital transformation in neuro-oncology, paradigmatically for other areas of medical care.

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.

HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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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) HORIZON-MSCA-2025-DN-01

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Coordinator

RUPRECHT-KARLS-UNIVERSITAET HEIDELBERG
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.

€ 870 816,96
Address
SEMINARSTRASSE 2
69117 Heidelberg
Germany

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Region
Baden-Württemberg Karlsruhe Heidelberg, Stadtkreis
Activity type
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

Participants (8)

Partners (8)

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