Optimising and predicting antidepressant efficacy using multi-omics analysis and AI-predictive tool
The OPADE project will identify critical biomarkers that support the decision-making process of healthcare providers by focusing on the microbiota–brain axis, which plays a central role in mental health and major depressive disorders.
By studying genetics, epigenetics, microbiomes and inflammatory networks to establish patient profiles, the project will predict and optimise antidepressant efficacy.
Digitalised value management for Circular Manufacturing Systems
The DiCiM project will demonstrate integrated digital solutions in four use cases in three industrial sectors (white goods, electronics and automotive) representing over two-thirds of the European Economy.
The project aim is to enable the reuse of products, parts and materials in these sectors.
Cutting-edge Quantum Computer Algorithms to Solve Strategic Industrial Problems
The EQUALITY project will bring together scientists, innovators and prominent industrial players to develop cutting-edge quantum algorithms tackling strategic industrial problems.
The consortium will develop a set of algorithmic primitives, including differential equation solvers, material simulation algorithms and quantum optimisers, that could be used as modules for various industry-specific workflows.
Disrupting the Migraine continuum of care for resource-constrained settings
The TARA project will disrupt the continuum of care and referral pathway by creating a more effective non-surgical solution that reduces cost and risk and, therefore, increases accessibility to more physicians and patients.
The consortium will develop a novel platform for treating chronic migraine that will be particularly applicable to resource-restricted environments and targeting underserved patients.
Unlocking Europe’s potential in critical raw materials
The AGEMERA project will perform local geological and geophysical surveys to map critical raw material resources in six EU Member States and Zambia.
The project will exploit a broad range of data to enhance the genetic mineral system models of deposits that contain critical raw materials, such as lithium and cobalt.
Saving forests to protect people and planet
The SWIFTT project will provide a scientifically sound and technically feasible way to help monitor and manage forest risks.
The project will enable forest managers to adapt to climate change with affordable, simple and effective remote sensing tools backed up by machine learning models.
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