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-training that facilitate few-shot adaptations. The research will show how the complementarity of event-triggered learning and meta-learning can drastically increase RL efficiency at test time. The co-design
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interactions need to be strengthened, integrating the extracted requirements into services, prototyping the necessary interfaces/software, and iteratively testing via case studies. The development
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a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a holistic multi-hazard risk
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programming (MATLAB/Python) and/or structural analysis software (e.g., OpenSees, ABAQUS) is highly desirable Willingness to participate in experimental testing campaigns and multi-national secondments
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process. Together, these innovations aim to make column generation more practical for solving real-world, large-scale optimization problems. These innovations will be tested within a structured software
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team consisting of scientific staff and PhD candidates from different disciplines and will collaborate with a small team of software developers working on AI prototypes and the required supportive
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experience in the field of small spacecraft (pico, nano or micro satellites) like in example design, development or operations of small satellite hardware or software. Applicants should also have good
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Experimentally validate and improve the developed channel model with over-the-air terrestrial and satellite test campaigns Carry out research in other related areas Disseminate results through scientific journals
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) IELTS: 6.5, with no section lower than 5.5 (only Academic IELTS test accepted) CAE/CPE: grade B or A. In extraordinary circumstances, formal documentation of language skills can be relinquished. In
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90 or IELTS test with a minimum score of 7.5 is required. Desired qualifications We would like you to have an average grade point for courses included in the Master’s degree of B or better in terms