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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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framework for AI in gynecological oncology. We integrate symbolic knowledge representation (Ontologies/Knowledge Graphs) with Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to create
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of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning
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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 with large-scale
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neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio-)Statistics
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conditions, to large field-scale experiments with wild and domestic grazers. These experiments will test hypotheses related to the effects on nutrient cycling of grazers with different body size and grazing
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and conduct experiments at various scales from laboratory manipulations of animal plant-soil systems including micro and mesocosms under varying environmental conditions, to large field-scale
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to the research and development of dexterous end effectors for a new 6G-based teleoperated surgical robotics system. This position stems out of the large scale 6G-Life project (https://6g-life.de/) and will give
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quantum computers and the ones using them. In this field, we are about to start a visionary and large-scale ERC Consolidator Grant Project and are going to become part of the Munich Quantum Valley