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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and
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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
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) Use co-regulation networks for gene function and protein–protein functional relationship prediction (guilt-by-association), and benchmark them against existing bulk co-expression resources Compare and
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environmentally friendly chemical production. Due to the cross-industry and cross-disciplinary nature of the topic, experts from various disciplines work closely together on this. Modern biotechnological methods
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, extending them with physics-based approaches, and adapting existing physics-integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring
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submanifolds and their temporal dynamics during behavior Leverage dimensionality reduction and regression models to isolate task-related submanifolds and their respective role for sensory processing and task
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t
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out research, working projects in Germany and to maintain contacts as part of DAAD's global alumni network. The programme will be discontinued on 31 December 2025. Applications are no longer being