consultancy assignment to develop and deliver training on Strengthening Africa's Multi-Hazard Early Warning Systems and Resilience to Hydromet Hazards

Expected Outcome: Project should contribute to all of the following expected outcomes: Research funders, policymakers and research communities in the social sciences and humanities (SSH) are provided with a multi-annual R&I programme on social transformations and resilience, responding to megatrends like climate change and biodiversity loss, digitalisation, demographic changes and unexpected shocks. Research investments on social protection and essential services, future of work, education and skills, and a fair transition to climate neutrality are increased. Stakeholders, including social partners and civil society, and policymakers are provided with evidence-based knowledge, tools and innovative solutions, which contribute to new policies and strategies for strengthening resilience, fairness, inclusiveness and social cohesion at European, national and regional level. Scope: Europe is undergoing critical social transformations driven by macro drivers of change, such as climate change, biodiversity loss, digitalisation and demographic change, which have been accelerated by events, such as the COVID-19 pandemic and Russia’s war of aggression against Ukraine. These transformations…

Expected Outcome: Europe's first long-term VET provider network for semiconductors; a larger and more diverse pool of qualified technicians; and increased intra- and extra-EU mobility of students and workers to fill skills gaps in semiconductor facilities. Scope: This call supports the establishment of a European-level federation of Vocational Education and Training (VET) providers focused on semiconductor industry roles. Projects should develop industry-aligned micro-credentials and reskilling programmes, with a strong emphasis on workers transitioning from other industrial sectors and on cross-border recognition of qualifications.

Expected Outcome: Project results are expected to contribute to all the following expected outcomes: Availability and integration of advanced, trustworthy, energy-efficient perception systems, exploiting technological advancements of Generative AI (GenAI) to enhance situational awareness and support safe decision-making; Enhanced Vulnerable Road User (VRU) safety, based on elevated, more temper-proof perception and understanding of their behaviour and intention predictions; Enhanced robustness of CCAM systems - both on-board and on the infrastructure side - in critical situations due to their training, virtual testing and validation in scenarios generated by GenAI, complementing existing scenario databases for the testing and validation of CCAM systems; Enhanced understanding of the relevance and limitations of using GenAI for CCAM; Tools and harmonised approaches for the use of GenAI in mobility technology development, training and validation, as well as for systemic applications such as traffic management and remote control, integrating them into existing approaches. Scope: Pilots and demonstrations using Level 3 and 4 vehicle services face major challenges in perception and…

Expected Outcome: Project results are expected to contribute to all the following expected outcomes: Development of systems that utilize real-time data analytics to dynamically respond to disruptions, allowing transport and infrastructure operators to adjust operations swiftly and efficiently; Development of predictive maintenance strategies applying innovative Generative AI solutions and utilizing other digital technologies to anticipate and prevent infrastructure and equipment failures; Development of guidelines and tools to support passenger transport operators and authorities to ensure minimal disruption in unexpected and critical situations and organise real life emergency simulations; Deployment of training programme for transport and infrastructure operators on how to handle transport disruption, including tools and catalogue with contingency planning for specific transport disruptions; Reduction of average passenger delay at corridor level (at least 20%) during planned disruptions, compared to the baseline and of (at least 40%) time needed from the generation to the dissemination of a response plan to different stakeholders (e.g. transport operators, passengers, citizens)…

Expected Outcome: Projects are expected to contribute to all the following expected outcomes: Effective and innovative methods for gathering, sharing and using large data sets in energy applications for the purpose of training AI models while ensuring privacy and security. Advanced and - wherever possible - open-source AI foundation models to support the digitalisation of the energy system, through improved grid observability, forecasting of supply and demand, advanced storage and renewables integration, demand side flexibility and energy efficiency. Enhanced cooperation, knowledge sharing and interoperability among energy system actors for secure and seamless data exchange. Advanced methodologies for AI model development ensuring that the results are FAIR (Findable, Accessible, Interoperable, Reusable) beyond the project ending, open source and accommodating for technology evolution. Scope: The quality of AI models depends strongly on the amount, quality, and representativeness of the data used for their training. Projects are expected to focus on developing, training and testing AI foundation models, using extensive data sets to accelerate the energy transition in key focus…

Expected Outcome: Practical chip design experience integrated into education at multiple levels; at least 1,000 students across Europe completing funded tape-outs over 4 years; stronger reskilling pathways for students from adjacent fields; and a sustainable, continent-wide pipeline of chip design talent to support Europe's semiconductor ambitions. Scope: This call funds a comprehensive Chip Design Skills Programme to lower barriers to entry in chip design and tape-outs. At least 60% of the budget must support hands-on tape-out experiences (via multi-project wafers) for university and secondary school students.

Rehabilitation of Data Collection Platforms and Associated Works in 18 Hydrological Stations