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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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algorithms for application parallelization, simulators and virtual platforms for application- and architecture exploration, hardware/software co-design and operating/runtime systems. Typical application
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tunnel testing and statistical modelling using extreme value theory, the project will yield predictive frameworks for structural risk under rare yet damaging wind conditions. The results will support
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to reason about software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new
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for scientific work strong programming skills and experience in software projects excellent written and spoken English skills TUD strives to employ more women in academia and research. We therefore expressly
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+C). You will extend the existing core-level GW+C implementation in the FHI-aims software package. Currently, this approach is limited to molecules; your task will be to adapt and expand it for
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-level GW+C implementation in the FHI-aims software package. Currently, this approach is limited to molecules; your task will be to adapt and expand it for application to 2D framework materials
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disciplines. You have proficiency in data analysis and statistical software packages. You have problem solving skills. You fit to us: if you have a strong interest in plant physiology and root biology. if you
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; safe handling of inert gas techniques; knowledge of analytical methods for molecular chemistry; Microsoft Office and chemical software handling; confident presentation of scientific results in English
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of models in existing simulation software conducting numerical studies, also on HPC systems Further specific tasks can be tailored to the attitude and interests of the PhD students/postdocs. Requirements