The detection of infectious disease emergence relies on reporting cases, i.e. indicator-based surveillance (IBS). This method lacks sensitivity, due to non or delayed reporting of cases. In a changing environment due to climate change, animal and human mobility, population growth and urbanization, there is an increased risk of emergence of new and exotic pathogens, which may pass undetected with IBS. Hence, the need to detect signals of disease emergence using informal, multiple sources, i.e. event-based surveillance (EBS). The MOOD project aims at harness the data mining and analytical techniques to the big data originating from multiple sources to improve detection, monitoring, and assessment of emerging diseases in Europe. To this end, MOOD will establish a framework and visualisation platform allowing real-time analysis and interpretation of epidemiological and genetic data in combination with environmental and socio-economic covariates in an integrated inter-sectorial, interdisciplinary, One health approach: 1)Data mining methods for collecting and combining heterogeneous Big data, 2)A network of disease experts to define drivers of disease emergence, 3)Data analysis methods applied to the Big data to model disease emergence and spread, 4)Ready-to-use online platform destined to end users, i.e. national and international human and veterinary public health organizations, tailored to their needs, complimented with capacity building and network of disease experts to facilitate risk assessment of detected signals. MOOD output will be designed and developed with end users to assure their routine use during and beyond MOOD. They will be tested and fine-tuned on air-borne, vector-borne, water-borne model diseases, including anti-microbial resistance. Extensive consultations with end users, studies into the barriers to data sharing, dissemination and training activities and studies on the cost-effectiveness of MOOD output will support future sustainable user uptake

Reliability and radiation damage issues have a long and important history in the domain of satellites and space missions. Qualification standards were established and expertise was built up in space agencies (ESA), supporting institutes and organizations (CNES, DLR, etc.) as well as universities and specialized companies. During recent years, radiation concerns are gaining attention also in aviation, automotive, medical and other industrial sectors due to the growing ubiquity and complexity of electronic systems and their increased radiation sensitivity owing to technology scaling. This raises the demand for dedicated design and qualification guidelines, as well as associated technical expertise. Addressing open questions linked to respective qualification requirements, the proposed training network “RADiation and Reliability Challenges for Electronics used in Space, Aviation, Ground and Accelerators” (RADSAGA) will for the first time bring together industry, universities, laboratories and test-facilities in order to innovate and train young scientists and engineers in all aspects related to electronics exposed to radiation. The expertise of the space and avionics sectors will be complemented with new and unique test facilities, design and qualification methodologies of the accelerator sector, promising for other application areas. Driven by the industrial needs, the students will be trained by established specialists in all required skills, and acquire expertise through innovative scientific projects, allowing to: (i) push the scientific frontier in design, testing and qualification of complex electronic systems for mixed field radiation environments (ii) establish related courses to train future engineers/physicists; and (iii) issue design and test guidelines to support industry in the field, protecting European competitiveness when radiation effects become as important as thermal or mechanical constraints for the aviation, automotive and other industrial sectors.

INNOMEM gathers some of the most recognised Membrane departments (>20) in Europe and acknowledged facilitators of technology transfer, corporate finance, funding and coaching, making available (i) the most promising and breakthrough manufacturing pilots and (ii) advanced characterization techniques and modelling together with (iii) non-technical services through this Test Bed: while relevant improvement metrics can be defined, the potential network of reachable stakeholders counts thousands of businesses on an international scale. Key facts are reported below. Within the scope of INNOMEM, main different types of membrane materials (polymeric, ceramic, metallic and nanocomposite), surface modification, membrane morphology and geometry and applications will be covered, providing for the first time a single entry point for industrial partners, mainly SMEs, aspiring to answer their concerns but with minimum investment costs and reduction of risks associated with technology transfer, while opening-up opportunities for demonstration of innovative nanomembranes in real life industrial problems (TRL7) and thus faster opening the market for these new products. The main KPIs for INNOMEM: Technical: 20% Membrane productivity improvement, 30% faster verification, >40% CO2 emissions and energy consumption reduction. Non-Technical: 10 Showcases, >15 Democases, >100 reachable SMEs and > 300 reachable investors. INNOMEM stems from the consideration that the development of products based on advanced membranes and nanomaterials require access to finance and an optimised business planning, relying on a sound prior analysis of the market, of the economic impacts and capacity of a company. The project aims at developing and organizing a sustainable Open Innovation Test Bed (OITB) for nano-enabled membranes for different applications. The OITB will also offer a network of facilities and services through a Single Entry Point (SEP) to companies (inside or outside Europe).

The EU faces the challenge to protect pelagic biodiversity over immense Overseas Countries and Territories. However, cost-efficient, safe and reproducible methods are still missing for assessing the diversity and abundance of pelagic megafauna (marine mammals, sea turtles and sharks). We will adapt image-based surveys and deep learning—a new method based on artificial intelligence—to pelagic megafauna. A Coral Sea Nature Park (CSNP) was recently created in New Caledonia Overseas Territory but this area still lacks reserves in pelagic ecosystems. We will propose a reserve network that optimises the trade-off between critical habitat protection of pelagic megafauna and fisheries economic profitability in this area. This network will directly advise decision makers of the CSNP and help fulfil IUCN target of 30% of oceans protected by reserves by 2030. Our objectives are: RO1—Detect pelagic megafauna, RO2—Predict pelagic megafauna hotspots, and RO3—Protect hotspots in the Coral Sea. To reach RO1, image-based surveys will be conducted and megafauna will be automatically detected on images using deep learning. To reach RO2, habitat models will be used to predict megafauna diversity and abundance as a function of environmental variables. To reach RO3, fisheries economic data will be incorporated to propose a reserve network optimising both conservation and economic needs. This project will be jointly hosted by the Marine Biodiversity Exploitation and Conservation Laboratory (MARBEC) and the Montpellier Laboratory of Informatics, Robotics, and Microelectronics (LIRMM), both part of the University of Montpellier. Supervised by D. Mouillot (MARBEC) and M. Chaumont (LIRMM), I will receive the best appropriate interdisciplinary training. After an enriching research experience in the U.S, I aim to integrate a long-term research position in Europe. This fellowship would be a superb opportunity to develop an independent and innovative research project in marine numerical ecology.

This proposal sprang from European scientists in both academia and industry who identified a common challenge: setting up a training frame to educate the next generation of imagers in complex biological systems (healthy & pathological), so they are able to master all major aspects of this competitive field and bring important innovations to universities and companies. The long-term goal of any initiative to image biological processes is reaching cellular or subcellular resolution in a complete organism. This is now possible using vertebrate embryos as models and the most recent technological advances as tools. ESRs will be trained by addressing the following scientific bottlenecks and challenges: -Preparing vertebrate embryos (rodent & zebrafish) for optimal imaging -Fine-tuning sensors, reporters and actuators to track cell types, cellular processes and behaviours in living organisms -Developing and implementing new imaging instruments -Analysing complex sets of big-data images to extract relevant information -Using processed images to design computational and mathematical models of development and pathologies -Comparing these models with experimental data and create a feedback loop improving the whole work chain from sample preparation to instrumentation and analysis. This interdisciplinary training is based on an intersectoral organisation of the consortium with partners from academia and companies that need these future experts to develop new instruments, screen drugs and chemicals in living systems and develop software to analyse and model medical images. The full training programme is based on an optimal balance between training through research and many network-wide training events, including conferences with physical presence, digital conferences and monthly videolink events. Consortium members are keen to implement both classical and original outreach activities (eg MOOCs, serious games, Lego designs) to bring state-of-the-art microscopy to the classroom.