<< Objectives >>The main objective of UPINFOOD project is to promote the transformation of the sector towards a new modernised vision of food that supplies nowadays demand in quality and healthy products while being sustainable, taking the most of new technologies and being a motor of innovation, thanks to education addressed to the key actors of this industry. Thus we intend to:-Develop a competence framework -Train professionals from the sector -Create collaborative tools -Equip VET trainers<< Implementation >>-Working seminars to better our mutual definition of key concepts-Focus groups with our target groups to assess their needs-Development of the project results -Implementation and testing of our results-Validation and certification of our results-Enrolment of further stakeholders thanks to a mainstreaming strategy that will support the spread of the UPINFOOD material and their integration among training offer in the food innovation field-Promotion of results<< Results >>Our expected results are:-A competence profile and learning pathway for the food (future) professionals relating to innovation, sustainability and empowerment.-An online training course including tests of competences, practical activities and collaborative elements -A train the trainer manual that supports the implementation of the course-A mainstreaming strategy-Durable impact on the local, regional and EU level in terms of transformation of food business models

Deploying algorithmic solutions in real-world applications raises two challenges. First, we need easy-to-use and universal algorithms. Second, we need to guarantee that algorithmic solutions can be understood by people using them. We address the first of these challenges in TUgbOAT project, which aims to deliver unified algorithmic tools. Here, we propose to develop tools that would address the second of these challenges. In many use scenarios, algorithms propose a solution to a human operator. The main challenge in such cases is to convince him to use the returned solution. Traditionally we think of algorithms in a black-box manner, i.e., as a tool to find a good solution. We do not expect algorithms to give a human-understandable explanation of why this is the best solution, or what alternatives exist or what are the bottlenecks. Nevertheless, we humans still tend to ask these questions even if we understand the algorithms that are used. Currently, we lack good tools that could explain the results of optimization algorithms, e.g., for the assignment problem. For practitioners, like ourselves, that work together with companies to deploy algorithmic solutions in real-world cases, the need to provide explainable algorithms becomes immanent. Here we will test and implement results developed in TUgbOAT that can be used to complement the algorithms with human explanations. In particular, we plan to: - enrich algorithms to give meaningful alternative solutions, - apply Shapley value methods to determine key solution elements, - work with perturbed inputs to create robust and more concise solutions, - generate concise decision trees that would explain steps taken by algorithms. This project aims to deliver the base parts of a software library that would give explainable algorithms. We plan to concentrate on the task assignment problem (i.e., matchings) where we already cooperate with companies.

<< Objectives >>The project targets to provide e-learning opportunities for youth to enhance or cultivate from scratch their professional, entrepreneurial and digital skills in the field of Cultural and Creative Industries by using creative and innovative technologies. The scope is to target youth with limited access to educational opportunities and unemployed (NEETs). The project aims to transform education in CCI with OER digital resources rendering it accessible to all youth communities.<< Implementation >>Youth Interests Verification/ EU report Youth Entrepreneurship Manual with training content and tips and tricks on CCI sectorPersonalised Skills Wheel for the custom training and knowledge validation of Youth (OER)A guide for youth workers/ educators/ mentors in the CCI sector3 Physical meetings for the consortium to work together in key dates of the project implementation Internal and External Piloting of the deliverables Promotional Events<< Results >>Tangible: The training methodology in CCI The Entrepreneurship Manual for youth in CCI The Personalised skills Wheel for the e-learning in CCIThe Guide for youth educators/ mentors in CCI/ workers Intangible: Raise the knowledge and skills of youth for employability and entrepreneurship. Enhancement of ICT-enabled education and e-learning strategies in the CCI sector.Qualitative training to underrepresented groups towards more inclusive and accessible educational opportunities.

Our solution is an innovative suite of hardware-agnostic quantum algorithms, along with their hardware-aware implementations. Our solution will consist of a complete, multi-level set of techniques that reduce quantum circuits’ size and improve their fidelity when executed on real quantum hardware. We will enable relevant business problems, too difficult for conventional computing, to be solved several years earlier. The exemplary solution consists of: 1) Application layer with problem reduced (classically) to the form best suited for QC. [In case of routing this is parallel TSP reduced, for example to max 2-SAT] 2) Optimisation layer containing features guaranteeing optimal representation given problem size. This includes our decomposition techniques for multi-control quantum gates 3) Implementation layer guaranteeing optimal use of the hardware (this includes hardware aware implementation, taking into account the topology (qubit connectivity) and native gate set.