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to be developed. One promising research direction is the use of physics-informed deep learning, such as physics-informed neural networks or deep neural operator networks. Tasks: Work in a team on national
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regeneration in the forest interior“we aim to develop innovative remote sensing approaches to enhance the mechanistic understanding of the effects of increasing forest disturbances on closed canopy forests
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differential equations (PDE) in the model to describe either environmental influences or a more detailed component behavior offers a possible solution to this challenge. The goal of the project is to develop
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PhD position in interpretable machine learning for dementia prediction. The project focuses on developing interpretable deep learning models for dementia prediction using multi-modal data, including MRI
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world is continuously sensed and/or explicitly shared to provide a model of 6G network environment and its users – encouraging design of algorithms, protocols, and systems that “intelligently” fuse data
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11.11.2024, Wissenschaftliches Personal In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed which provides information on both the health and the
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Design”, led by Prof. Ledendecker at the HI-ERN in Erlangen. The department specializes in the development of metal-based inorganic catalysts aimed at advancing the global energy transition. Our research
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performance. Note: The workplace is not entirely barrier-free nor wheelchair accessible (listed old building). Your application To apply, please prepare a cover letter expressing your motivation and preparation
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Freising-Weihenstephan. - Polarity regulation by protein kinases during stomata development - Phospho-regulation of the plant cytoskeleton - Developmental adaptations for plant growth in soil. We seek highly
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scale Research budget for conference attendance and professional development. Opportunities to collaborate with leading industry partners. Access to world-class academic resources. Application Process