Recent events have demonstrated the need for readiness for medical supply and equipment rapid manufacturing repurposing. Eur3ka will deliver a trusted and unique capability to plug and collectively respond to a sudden demand increased in a coordinated and effective manner at unprecedented scale. Eur3ka mission is to bring together most recent R&I results in (1) Industry 4.0 standards, open automation modular manufacturing production line enablers; (2) industrial international common data space enablers and digital infrastructures; (3) global on-demand and manufacturing as a service platforms; (4) connected and smarter supply networks, and global medical supplies and equipment repositories; (5) the vibrant European and Global network of manufacturing DIH network innovation services and open experimental facilities. The main ambition of Eur3ka is to enable and facilitate global and fair access to (1) a Plug & Respond (P&R) repurposing resource coordination framework for pandemic crisis response, (2) a common open standardized modular manufacturing reference architecture and solutions, and (3) top digitally sovereign cross-sectorial manufacturing networks and capacities that should allow to connect global manufacturing and supply chain capabilities and medical knowledge on-demand and as-a-Service across the globe in an IP-responsive manner to ensure rapid manufacturing repurposing for an increased and sudden demand of medical supplies and equipment. Eur3ka builds and extends the existing Global Network of Advanced Manufacturing Hubs (AMHUBs) to leverage a comprehensive COVID response based on solid socio-tecno-economic pillars that bring together advanced manufacturing and digital enablers that will raise robustness, redundancy, resourcefulness, response, and recovery against current and future pandemics. Eur3ka vision builds on and accelerate current digital transformation industry 4.0 efforts, as well as flexible regulations and tailored workforce re-/up- skilling

Climate change is one of the most urgent problems facing mankind. Implementation of the Paris climate agreement relies on robust scientific evidence. Yet, the uncertainty of non-greenhouse gas forcing associated with aerosol-cloud interactions limits our constraints on climate sensitivity. Radically new ideas are required. While the majority of forcing estimates are model based, model uncertainties remain too large to achieve the required uncertainty reductions. The quantification of aerosol cloud climate interactions in Earth Observations is thus one of the major challenges of climate science. Progress has been hampered by the difficulty to disentangle aerosol effects on clouds and climate from their covariability with confounding factors, limitations in remote sensing, very low signal-to-noise ratios as well as computationally, due to the scale of the big (>100Tb) datasets and their heterogeneity. Such big data challenges are not unique to climate science but occur across a wide range of data science applications. Innovative techniques developed by the AI and machine learning community show huge potential but have not yet found their way into climate sciences – and climate scientists are currently not trained to capitalise on these advances. The central hypothesis of IMIRACLI is that merging machine learning and climate science will provide a breakthrough in the exploration of existing datasets, and hence advance our understanding of aerosol-cloud forcing and climate sensitivity. Its innovative training plan will match each ESR with supervisors from climate and data sciences as well as a non-academic advisor and secondment and provide them with state-of-the-art data and climate science training. Partners from the non-academic sector will be closely involved in each of the projects and provide training in a commercial context. This ETN will produce a new generation of climate data scientists, ideally trained for employment in the academic and commercial sectors.

The increasing uptake of renewable energy sources and liberalization of electricity markets are significantly changing power system operations. To ensure stability of the grid, it is critical to develop provably safe feedback control algorithms that take into account uncertainties in the output of weather-based renewable generation and in participation of distributed producers and consumers in electricity markets. The focus of this proposal is to develop the theory and algorithms for control of large-scale stochastic hybrid systems in order to guarantee safe and efficient grid operations. Stochastic hybrid systems are a powerful modeling framework. They capture uncertainties in the output of weather-based renewable generation as well as complex hybrid state interactions arising from discrete-valued network topologies with continuous-valued voltages and frequencies. The problems of stability and efficiency of the grid in the face of its changes will be formulated as safety and optimal control problems for stochastic hybrid systems. Using recent advances in numerical optimization and statistics, provably safe and scalable numerical algorithms for control of this class of systems will be developed. These algorithms will be implemented and validated on realistic power grid simulation platforms and will take advantage of recent advances in sensing, control and communication technologies for the grid. The end outcome of the project is better quantifying and controlling effects of increased uncertainties on the stability of the grid. The societal and economic implications of this study are tied with the value and price of a secure power grid. Addressing the questions formulated in this proposal will bring the EU closer to its ambitious renewable energy goals.

The proposed research project aims at implementing enzymatic activity in a de novo designed, unbiased protein scaffold. First, simplified active site arrangements deduced from two previously evolved model enzymes (Kemp eliminase and retro-aldolase) will be implanted in the scaffold. This will allow evaluating the extent to which a fully computationally designed and naïve protein can be functionalized and evolved. A recently established fluorescence-based microfluidics setup will be utilized to screen large DNA libraries of several million clones per round of laboratory evolution. The artificial protein scaffold, kindly provided by Prof. David Baker (University of Washington), has been designed to adopt a minimalist TIM barrel fold, which is the most abundant and diversely evolved protein fold found for natural enzymes. Here, the substrate binding pocket is usually formed by an extended loop region on one side of the scaffold, which is not yet present in the naïve, designed variant. Thus, in the second step, I will generate a randomized library of loop fragments, insert into the scaffold and screen for improved activity. This approach can be extended from the retro-aldolase model reaction towards synthetic aldolases that catalyze the stereospecific formation of a new carbon-carbon bond. Ultimately, the aim is to evaluate whether loop libraries are an appropriate tool to broaden the substrate scope of these enzymes. Furthermore, the proposal contains a second, independent approach to equip the artificial scaffold with novel enzymatic functionality. Here, I plan to design a covalent dimer of two artificial TIM barrels carrying a cofactor-dependent active site in the dimeric interface. This work will not only generate fundamental insight into the evolution of catalytic activity, it also has great potential to contribute to the development of general strategies for creating enzymes with novel functionality, and thus, prospective applications in industry or medicine.