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TUM

Technical University of Munich
Country: Germany
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823 Projects, page 1 of 165
  • Funder: EC Project Code: 306754
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  • Funder: EC Project Code: 101058905
    Funder Contribution: 189,687 EUR

    This action aims to deliver innovative Lithium (Li)-operated gas sensor devices. The low-power (i.e., low-temperature), low-cost, fast-response miniaturized gas sensor will monitor gas pollutants based on fast ion-conducting Li-based chemistries, originally developed for next-generation energy storage systems. It remains surprising that despite their high conductivity and stability, Li-garnet Li7La3Zr2O12 (LLZO) solid electrolytes were mostly first integrated as large-scale ceramics in solid-state batteries, with their integration in sensors to replace classic oxygen conductors lagging behind. The fast ion-conduction characteristics of Li-based materials unlock the possibilities of cost-effective, low-power, multi-sensing arrays with a fast response, new sensing-electrode chemistries and an expanded scope of gases. The applicant will develop and implement a cheap and scalable ceramic processing concept of a fully Li-based sprayed sensing device operating at low temperature to assure a transition of research to society. This project will contribute to the major driving force behind EU's mission, finding a low-carbon energy solution to minimize pollution effect on global health and the environment, by providing tools for toxic gas sensing which will a) consume less energy and b) enable distributed sensing. Where sensing and power supply start to synergize, new opportunities for multifunctional devices based on similar chemistry (e.g., Li-garnet) to store energy or detect gasses, are emerging simply by the choice of electrodes and electrochemistry. The synthesis, chemical requirements, processing, and electrochemical characteristics upon gas sensing, vital for future sensor-nose technology and hardware in mobile commercial applications, remain unclear and are south to be pioneered through this action.

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  • Funder: EC Project Code: 883818
    Overall Budget: 1,971,300 EURFunder Contribution: 1,971,300 EUR

    Modern science increasingly relies on insights gained from sophisticated analyses of large data sets. An ambitious goal of such data-driven discovery is to understand complex systems via statistical analysis of multivariate data on the activity of their interacting units. Probabilistic graphical models, the topic of this project, are tailored to the task. The models facilitate refined yet tractable data exploration by using graphs to represent complex stochastic dependencies between considered variables. Models based on directed graphs, in particular, provide the state-of-the-art approach for detailed exploration of cause-effect relationships. However, modern applications of graphical models face numerous challenges such as key variables being latent (i.e., unobservable/unobserved), lacking temporal resolution in studies of feedback loops, and limited experimental interventions. Often arising in combination, these issues generally result in observed stochastic structure that cannot be characterized using the established notion of conditional independence. As a result, we are left with only a partial understanding of which aspects of a system can be inferred from the available data, and we lack effective methods for fundamental problems such as inference in the presence of feedback loops. The aim of the new project is to move beyond conditional independence structure to obtain a deeper understanding of the inherent limitations on what can be inferred from imperfect measurements, and to design novel statistical methodology to infer estimable quantities. The unique feature of the proposed work is a focus on algebraic relations among moments of probability distributions and the subtle statistical issues arising when such relations are to be exploited in practical methodology.

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  • Funder: EC Project Code: 754462
    Overall Budget: 11,328,000 EURFunder Contribution: 5,664,000 EUR

    Combining complementary research & innovation strengths with unique infrastructures across four leading European Universities of Science & Technology will provide the basis for an internationally competitive fellowship programme. The new EuroTechPostdoc programme aims at attracting excellent experienced researchers from all over the world through an open, transparent and structured recruitment process. EuroTechPostdoc will provide exceptional research and training opportunities for up to 80 experienced researchers funded for a 2-year period (divided in 2 calls, 10 fellowships per participant per call). The fellows will develop their skills in interdisciplinary collaboration projects between at least two participants from two different European countries, and will be provided with the opportunity to transfer knowledge in entrepreneurship activities or by collaboration with public and industrial organisations from the combined networks of the participants. The research programme has been carefully selected to provide the fellows with the complementary strengths, established networks and industrial collaboration in areas of high relevance to the EU’s industrial leadership: Health & Bioengineering, Smart & Urban Mobility, Data Science & Engineering, High Performance Computing, Entrepreneurship & Innovation. The fellows will gain access to the excellent and complementary training options offered at the four participants, and will be supported by a personal supervision and mentoring scheme, as well as excellent local support structures. The EuroTechPostdoc 3-days Workshop focuses on strengthening the fellows’ skills and network, preparation ahead of their careers and to foster further collaborative projects between the fellows. Together with outstanding working conditions that support both family and career, the programme will thus provide the fellows with unprecedented possibilities to diversify their career options for a leadership position within the ERA.

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  • Funder: EC Project Code: 282250
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