The main goal of TraDE-Opt is the education of 15 experts in optimization for data science, with a solid multidisciplinary background, able to advance the state-of-the-art. This field is fast-developing and its reach on our life is growing both in pervasiveness and impact. The central task in data science is to extract meaningful information from huge amounts of collected observations. Optimization appears as the cornerstone of most of the theoretical and algorithmic methods employed in this area. Indeed, recent results in optimization, but also in related areas such as functional analysis, machine learning, statistics, linear algebra, signal processing, systems and control theory, graph theory, data mining, etc. already provide powerful tools for exploring the mathematical properties of the proposed models and devising effective algorithms. Despite these advances, the nature of the data to be analyzed, that are “big”, heterogeneous, uncertain, or partially observed, still poses challenges and opportunities to modern optimization. The key aspect of the TraDE-Opt research is the exploitation of structure, in the data, in the model, or in the computational platform, to derive new and more efficient algorithms with guarantees on their computational performance, based on decomposition and incremental/stochastic strategies, allowing parallel and distributed implementations. Advances in these directions will determine impressive scalability benefits to the class of the considered optimization methods, that will allow the solution of real world problems. To achieve this goal, we will offer an innovative training program, giving a solid technical background combined with employability skills: management, fund raising, communication, and career planning skills. Integrated training of the fellows takes place at the host institute and by secondments, workshops, and schools. As a result, TraDE-Opt fellows will be prepared for outstanding careers in academia or industry.

ySKILLS starts from the observation that digitisation is changing society and requires a new set of digital skills, which many children and adolescents currently do not master. This can negatively affect their educational, informational and social inclusion and wellbeing. Longitudinal and robust academic research on children’s and adolescents’ digital uses, the use context and its impact is lacking on national and European levels. ySKILLS examines risks and opportunities related to children’s and adolescents’ (aged 12 to 17) ICT uses and their digital skills to understand how to purposefully use ICTs towards greater cognitive, physical, psychological and social wellbeing. We offer a critical perspective on the notion of skills itself: by extending traditional conceptions of skills, by recognising children’s critical views on their skills as young citizens with agency, voice and rights. ySKILLS will predict which children are more at risk of having low levels of wellbeing because of their ICT use, and to understand how digital skills can function as building resilience against negative impacts. This results in a comprehensive, evidence-based explanatory and foresight model predicting the complex impacts of ICT use on children’s and adolescents’ wellbeing in Europe, and the role of digital skills that can enhance their wellbeing. ySKILLS will conduct a longitudinal three-wave survey in six countries, selected based on their ranking as low, medium and high on the 2018 Digital Economy and Society Index. Adding to this survey, cognitive wellbeing will be investigated with fMRI in two countries. ICT use patterns will be analysed among at-risk groups in in-depth studies in six complementary countries. Through an effective dissemination strategy and practice and policy recommendations, framed in terms of children’s rights, the interdisciplinary ySKILLS consortium will strengthen the necessary interaction among the relevant stakeholders and practitioners involved.

We observe the world around us predominantly through the measurement of optical intensity. Although powerful, this leaves the other fundamental optical degrees of freedom, phase and polarisation massively under-utilized. Our tendency to solely use intensity results from the static sensor technology that is available, which offer very limited ability to dynamically reconfigure their function or perform any optical processing. In Super-Pixels we will co-develop a new integrated sensor platform that will revolutionize the way we process light to allow the full utilization of its fundamental properties. Redefining the core functionality of our sensor technology will radically impact the technology that is deployed in a broad spectrum of cross-disciplinary areas such as nano-particle detection, compact atmospheric corrected imaging systems, endoscopy, coherent communications and on-chip processing of structured light. This vision will be enabled by a compact and multi-functional photonic integrated chip that would be installed into phones, microscopes, cameras, communication and environmental monitoring systems, becoming central part of the way we collect and process optical information. In Super-pixels, we will create such an integrated photonics device that is based on a mesh of several hundred Mach-Zehnder interferometers, which will be used to dynamically map phase and polarization, with the ability to fully transform any optical field incident. A revolutionary prototype system will be delivered that will partner our Super-Pixels chip with a commercially available camera to enhance its functionality within a single frame of a camera. This prototype will support a number of potential applications that include visualising normally invisible nano-particles through phase mapping, imaging through multimode optical fibres, reconfigurable quantum communication links and mapping of airflow and particulates through phase and polarisation retrieval.

The BioDiMoBot project will deliver a system for autonomous, long-term robotic assessments of aquatic biodiversity and ecology. This project will develop an innovative measurement system to monitor biodiversity and provide insight into the drivers of ecosystem degradation. Additionally, it will assess organismic and environmental stress levels by using novel biohybrid sensors. With the BioDiMoBots' ability to perform autonomous and automated multimodal long-term data collection, the project will create a user-friendly robotic tool that provides detailed insights into the ecological health of aquatic environments. The project will be driven by the needs of potential stakeholders such as researchers, small and medium-sized enterprises (SMEs) in water-related industries and policy makers. Field operations will demonstrate the transformative power of the BioDiMoBot systems by monitoring aquatic ecosystems over a long period of time. The collected data will be highly accessible to enable interdisciplinary collaboration between scientists, society and policy makers. We will respond to the need to disseminate the new methodology to a wide audienThe proposed "BioDiMoBot" project aims to enhance Biohybrid sensors focusing on biodiversity measurement. Key goals include tailoring sensors for comprehensive water column investigation, ensuring long-term autonomy, establishing efficient data transfer, and integrating classical aquatic and biohybrid sensors for ecosystem evaluation. The robotic system comprises a "surface unit" with solar cells for energy and communication, a "ground unit" for microbial activity investigation, ropes connecting these elements, and "rope-climbing modules" with additional sensors for sampling. This integrated system offers a precise and adaptable solution for biodiversity monitoring. Emphasis will be placed on advancing the concept of "biohybrid robots" through market analysis, stakeholder engagement, and company founding.