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computer science with very good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience
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methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using methods you develop before being transferred to a knowledge graph
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) offers two positions as Research Associate / PhD Student (m/f/x) (subject to personal qualification employees are remunerated according to salary group E 13 TV-L) starting as soon as possible
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related field Sound knowledge in the field of artificial intelligence and machine learning Ideally experience with knowledge graphs, semantic search, graph neural networks (GNNs), explainability
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and use machine learning methods (mainly graph neural network architectures) to design representations and transferable energy models for proteins and materials. The position will serve to develop your
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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international partners. Become part of our team PhD student (f/m/d) Work schedule: 65 % of the regular weekly working hours (currently 25 hours and 37 minutes) Duration: at the earliest possible date
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(graph neural networks, diffusion models) with quantum chemistry and molecular simulations, the project aims to accelerate bottom-up material discovery for applications ranging from life sciences
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Master's degree / PhD programme No Joint degree / double degree programme Yes Description/content The statistical physics of complex systems is a very broad field ranging from the study of quantum phenomena
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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures