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10.03.2023, Wissenschaftliches Personal We are looking for a motivated Ph.D. student or post-doc interested in developing novel processes that boost the production of bio-based liquid fuels and
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by cosmopolitanism, mutual appreciation, thriving innovation and active participation. For TUD diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we
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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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Forschungsinstitut für Nutztierbiologie (FBN) | Dummerstorf, Mecklenburg Vorpommern | Germany | about 18 hours ago
, climate, video) on different pasture sites Knowledge in the areas of experimental design and statistical data analysis Good written and spoken German and English language skills Driving license class B and
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Devices - Photovoltaics) and high-performance computation (IET-3: Institute of Energy Technologies - Theory and Computation) towards the overarching aim of implementing an inverse design approach for novel
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interdisciplinary collaboration. That’s why our projects are designed specifically to connect diverse scientific fields and foster cross-institutional collaboration, enabling you to benefit from the combined
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collaborations. That’s why our projects are designed specifically to benefit from collaboration among scientific experts from various fields and disciplines, enabling you to benefit from the combined supervision
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part-time with 67 %. Your key tasks Fabrication and characterization of porous materials. Developing new methodologies in the field of nanofluidics. Design and implementation of nanofluidic experiments
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complex transport systems: Perception: Developing robust models to handle noisy, complex spatiotemporal data, integrating physical constraints, and generalizing from limited labels. Decision: Designing
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on the following tasks with either with a stronger model-development or application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable