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, for the development and operation of space missions. LUX benefits from an extensive international network of partner institutions through its participation in major projects such as ALMA, SKA, ELT, HESS, CTA, SVOM
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, signals, measurement data, and biomedical data. Current research topics include: causal analysis in time series complex systems and networks, chaos, and fractal-based methods, EEG analysis, computational
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the Fellows4Fungi programme at the Leibniz-HKI (Jena, Germany). Fellows4Fungi - Exploring fungal pathogens within unexplored microbial networks is an international postdoctoral programme co-funded by the Marie
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, and climate projections depends critically on the adequate representation of land-atmosphere (L-A) feedbacks. These feedbacks are the result of a highly complex network of processes and variables
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to publish, expand your professional network, and develop expertise at the intersection of systems modeling, sustainability, and industrial practice. Job requirements A PhD in systems engineering, industrial
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. This project is funded by the European Space Agency (ESA) and is and is fixed-term for 24 months. The project “TMNT – Threat Monitoring Networked Team” will develop multi-robot scouting approaches to explore and
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, Switzerland, and Germany, with state-of-the-art infrastructure and outstanding opportunities for international networking and career development. The animal experiment will be running at the AO Research
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networking and career development. Candidates should hold a MD, DVM, or PhD degree, and should have interest in the bone-kidney axis and in spatial biology. Experience with mouse models is desirable
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IWRI - Postdoctoral Researcher in Bioinspired Circular Conversion of Multi-source Biomass Feedstocks
a multidisciplinary and multicultural team. Excellent networking ability and organizational skills. Experience in co-supervision and coordination of master/PhD students. Commitment to lab safety and
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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and