Artificial intelligence (AI), which is ubiquitous, not only affects the technical solutions in which it is used, but also has a macro-economic significance. The smart technical solutions that are used for smart manufacturing, smart cities, smart healthcare, smart mobility and in many other domains are interrelated and that means that AI has become a part of the macroeconomics that stimulate the economic growth of European society. Therefore, the research on AI development must be conducted with an understanding of its holistic impact and with a vision of how the AI can improve our lives. The sub area of AI Machine Learning (ML) makes it possible for computers to learn from data inputs and to use statistical analysis in order to provide output values that are contained within a certain range. ML has been used in many domains and applications, e.g., prediction, recommendation, image/speech recognition, medical diagnosis financial trading, among others.

TUAI’s work plan is composed of four research WPs, each of which focuses on a specific research areas and is complementary to other WPs. The research horizon of TUAI as a whole will enable the answers to the 13 individual research questions (RQs) faced by doctoral candidates (DCs) to be found in accordance with the training via a research model. The scope of the individual WPs reflects the components that are necessary to implement AI-based solutions in a real application use case and for the TUAI associated partners (AP) to solve real problems, which will use the methods, algorithms and services that are developed under the project in their products. The division of the research areas into WPs corresponds to the main research competences that are represented by the individual groups of experienced researchers (ER): Time Series Analysis (WP2), Sensor Fusion (WP3), Federated Learning (WP4) and the sustainability and trustworthiness of the AI solutions (WP5). In this way, each of the 13 Doctoral Candidates (DC) will receive excellent preparation and scientific workshop skills that will be necessary to answer the RQ that are stated through an individual project in accordance with Table 3.1f, excellent knowledge in the main research area through cooperation with the other DCs that are performing tasks in a given WP and gaining research experience in the related areas being developed by the other WPs via the Networking and Sharing (WP6).

Research WPs:

Time Series Analysis

Sensor Fusion

Federated Learning

Sustainability and Trustworthiness of the AI solutions