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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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Physics 1. Necessary requirements: 1) Ph.D. in the field of physical sciences 2) good programming skills, knowledge of C/C++, Python in the context of physics, knowledge of Linux/Unix 3) scientific
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Physics 1. Necessary requirements: 1) Ph.D. in the field of physical sciences 2) good programming skills, knowledge of C/C++, Python, Linux/Unix environment, ROOT package 3) scientific achievements in
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to (i) develop and adapt state-of-the-art methods from data science and (ii) apply the acquired knowledge to the research domains of the respective Helmholtz centers. The research will be accompanied by
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. Proficiency in MS Office programs, particularly Word and Excel; additional knowledge of Origin and MATLAB is preferred. English communication skills, both oral and written is required. Additional scientific
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how developmental dynamics can both open up and restrict evolutionary possibilities, and how this knowledge can help us better understand, predict, and even influence evolutionary change. We approach
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. Currently, research activities focus on applying research methods from experimental psychology and cognitive science to augment human intelligence with AI. Your experience and ambitions eligible for PhD study
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Faculty of Science The Faculty of Science (FNWI), part of Radboud University, engages in groundbreaking research and excellent education. In doing so, we push the boundaries of scientific knowledge
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application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable semantic querying and reasoning over materials-science/physics corpora Developing
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, turning geodata into new answer maps. We use knowledge graphs to model these transformations and apply AI methods to scale them across large map repositories, enabling users to explore many ways maps can be