Project description
Understanding machine learning for sustainability
Artificial intelligence (AI) and related technologies, such as smart systems, smart manufacturing, and smart cities, have paved the way for significant opportunities for advancing sustainability and energy efficiency. Supported by the Marie Skłodowska-Curie Actions programme, the TUAI project aims to develop a training programme to educate doctoral candidates on machine learning, its advancements, and how to leverage technological improvements for the benefit of European producers. Additionally, the project will apply these advancements to optimise smart services and devices, improving their sustainability and energy consumption throughout manufacturing and their use in smart cities or other smart methodologies.
Objective
The focus of the TUAI project will be on the highest quality doctoral training on sustainable the AI that is used in smart manufacturing, smart cities, smart healthcare and smart mobility. This will be achieved by multilateral academic and industry trainings for a new generation of creative, entrepreneurial and innovative researchers – Doctoral Candidates (DCs), who will be able to face both the current and future challenges that are associated with the development of Machine Learning (ML) and who will be able to effectively use the latest advances in artificial intelligence and data mining in order to ensure the competitive advantage of the European producers that will use the practical solutions from the TUAI project. The project will focus on ML-based smart services that should first meet the customers’ needs but with the same importance should focus on protecting the natural environment by creating smart services dedicated for smart mobility, to reduce energy consumption by smart cities and to avoid the losses and suboptimal use of resources in smart manufacturing. The scope of the research will be organised under four research areas (RA): (i) Time Series Analysis, (ii) Sensor Fusion, (iii) Federated Learning and (iv) the sustainability and trustworthiness of the AI solutions. The precise challenges for each RA will be expressed by research questions (RQ), which will be resolved by the individual DC projects. The RQs will not be separate areas of research but will be complementary and intertwined components. Therefore, it is not sufficient to train DCs in one RA and ignore another. The TUAI project will provide holistic research training through secondments and network-wide training activities. The proposed approach will ensure excellent research training for the thirteen DCs who will be prepared for future work both in academic and non-academic sectors and will be able to face the challenges that are associated with the different application areas.
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.
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.
- engineering and technology civil engineering urban engineering smart cities
- natural sciences computer and information sciences data science data mining
- natural sciences computer and information sciences artificial intelligence machine learning
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
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.
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.
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
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2023-DN-01
See all projects funded under this callCoordinator
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.
44-100 GLIWICE
Poland
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.