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Field
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) Research area: Large Language Models (LLMs), knowledge graphs (KGs), commonsense knowledge Tasks: foundational or applied research in at least one of the following areas: LLMs, KGs, knowledge extraction
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. He/she/they will learn and apply state-of-the-art molecular and cell biology technologies established in our team, ranging from in vivo disease models to multi-omics and single cell analysis
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the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by the German Research Foundation (DFG), at the University
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Malignancies: From Mechanisms to Therapies”. In this CRC we will focus on myeloid malignancies as a model to dissect the various molecular mechanisms that enable and regulate cancer cell plasticity in AML. Our
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. Furthermore, we use the olfactory network as a model to study the dynamics of neuronal development, synaptogenesis, neuronal degeneration, and regeneration. Our research is complemented by behavioral studies
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mobile at times; Fit and healthy – free of charge preventive medical check-ups and a wide-ranging subsidised sports programme Work-life balance – flexible working time models, plus BGM (Betriebliches
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to the maintenance of our biosphere, yet we have only a fragmentary understanding of the diversity and function of the microbial life in our oceans. The study of marine microbiology involves research into fundamental
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1