The project’s overall goal was to enhance capacity building in Higher Education ensuring high quality educational curricula that meets professionals, employers and socio-economic needs of Tunisia during a timeframe of 42 months, from 15th October 2017 to 14th April 2021.The specific objectives were:•To update Master curricula and improve the quality of teaching and learning at the University of Tunis El Manar, University of Sfax, University of Carthage in line with the Bologna Process requirements and the Europe 2020 Strategy for smart, sustainable and inclusive growth;•To build a more collaborative network between HEIs and local institutions/companies on integrated water and agro-ecosystem management.MAYA aimed at increasing the professionalism of young students by supporting the development of a new postgraduate Master on integrated water and agriculture management (IWAM) in the three involved Tunisian universities. In particular, the project intended to improve institutional and individual capacities and reduce the existing Tunisian constraints linked to the lack of scientific knowledge and technical expertise able to cope with complex issues. The new curriculum focused specifically on the innovative approaches to manage quality and quantity of water resources and on the existing linkages between agriculture, water management and environment. The project has been developed carrying out the following steps:1) An updated needs assessment of the curricula of the involved Tunisian universities related to water and agroecosystem management has been done at project beginning;2) three new high quality Masters on IWAM has been defined, developed and accredited in July 2019, one in each Tunisian Universities3) a new e-learning environment was implemented and new e-learning materials were created by the European HEIs; 10 e-learning modules were uploaded in the interactive e-learning platform, created and used to link the learning activities among the local HEIs and disseminate results and knowledge to other Tunisian universities and stakeholders;4) 30 Tunisian teachers were involved in an active training where they learnt new teaching and learning methodologies in the field of agricultural and water management; 5) 74 students have been enrolled (in total of the 2 Master edition) in the GIREAD Master advancing their technical skills on techniques and instrument for new IWAM strategies;6) a VR educational game has been developed on water analysis management in a scientific virtual laboratory, enhancing students’ cognitive capacities.7) 17 professors and 51 students participated in the field visits in the North and South of Tunisia realised by the three Tunisian Universities, despite the difficulties linked to the COVID19 pandemic. It was an enriching and fundamental experience to complete and better understand classroom studies. The practical use of the Trimble Juno GPS and the 3320 multiparameter probe gave to the student the opportunity to use part of the equipment not only in class but also in the field.The project was carried out following Workpackages:1. Preparation - Needs assessment2. Development - Curricula Development3. Development – Master Accreditation4. Development - Implementation of learning environment5. Development - Teaching and learning activities6. Development - Implementation of the new Master on Integrated Water and AgricultureManagement (IWAM) - Gestion IntégRée de l'Eau et Agriculture Durable (GIREAD)7. Quality Plan8. Dissemination and Exploitation – Dissemination and collaborative network9. Management – Project management

The SHERLOCK project aims to boost innovation at European level by designing and implementing an original and advanced educational framework based on microcredentials for upskilling the workforce and lifelong learning. It will integrate and combine a multidisciplinary green and digital skills portfolio towards the cooperation and knowledge exchange flow across different stakeholders, bolstering job creations and supporting the ambitious target of mass energy building retrofitting. Fostering retrofitting in the building sector, which accounts for 40% of the EU primary energy consumption and 35% of the carbon emissions, is paramount to achieve the EU decarbonisation targets by 2050. This requires a skilled workforce able to navigate through the multidisciplinary dimensions of building energy retrofitting, encompassing both technical, financial, and societal aspects.SHERLOCK aims to tackle this challenge by creating a MOOC-based MicroMaster programme and a VET oriented short training course based on microcredentials and case study education, targeting students and professionals from both the building energy retrofitting and financial sectors. The project will help overcome the skills mismatch between financial operators and project developers. The programme will be codesigned with VET providers and stakeholders through the setup and deployment of the SHERLOCK Knowledge Centres, which will act as a reference point for exchanging ideas and as a lasting alliance between universities, businesses, VET providers and public institutions. SHERLOCK will define the learning objectives, design the programme content & materials, implement and monitor the acquisition of the green and digital skills required in the building energy renovation sector. Finally, it will develop guidelines for educators in higher education institutions and VET providers on how to co-design and implement innovative MicroMaster programmes to support lifelong learning and upskilling of the labour market.

Deep Learning (DL) algorithms are an extremely promising instrument in artificial intelligence, achieving very high performance in numerous recognition, identification, and classification tasks. To foster their pervasive adoption in a vast scope of new applications and markets, a step forward is needed towards the implementation of the on-line classification task (called inference) on low-power embedded systems, enabling a shift to the edge computing paradigm. Nevertheless, when DL is moved at the edge, severe performance requirements must coexist with tight constraints in terms of power/energy consumption, posing the need for parallel and energy-efficient heterogeneous computing platforms. Unfortunately, programming for this kind of architectures requires advanced skills and significant effort, also considering that DL algorithms are designed to improve precision, without considering the limitations of the device that will execute the inference. Thus, the deployment of DL algorithms on heterogeneous architectures is often unaffordable for SMEs and midcaps without adequate support from software development tools. The main goal of ALOHA is to facilitate implementation of DL on heterogeneous low-energy computing platforms. To this aim, the project will develop a software development tool flow, automating: • algorithm design and analysis; • porting of the inference tasks to heterogeneous embedded architectures, with optimized mapping and scheduling; • implementation of middleware and primitives controlling the target platform, to optimize power and energy savings. During the development of the ALOHA tool flow, several main features will be addressed, such as architecture-awareness (the features of the embedded architecture will be considered starting from the algorithm design), adaptivity, security, productivity, and extensibility. ALOHA will be assessed over three different use-cases, involving surveillance, smart industry automation, and medical application domains