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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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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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analysis (SEC, spectroscopy, X-Ray, NMR, MS) is required. • Practical experience in modelling and design of proteins (Rosetta, Alpha-Fold, docking, molecular dynamics, etc.) will be positively
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. The BEAM projects cover a wide spread of topics, including theoretical physics and chemistry work. For example, our targeted syntheses are supported by models of self-assembly for specific types of molecules
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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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the training of state-space models Collaborate closely with our internal partners at PGI-14 (Neuromorphic Hardware Nodes) and international academic and industry partners Publish research articles and regular
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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
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optimization or discrete algorithms. Profound mathematical modeling and programming skills. Experience with the design and analysis of graph algorithms or multiobjective optimization models is a plus. Very good
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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