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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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are developed that prioritize interpretability and reduce data dependency by imposing desirable constraints on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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for proposing interpretable, synthesis-proximal modifications to known materials. Create generative models for material discovery adhering to strict physical constraints needed for stable and synthesizable
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regression models to isolate task-related submanifolds and their respective role for sensory processing and task performance Analysis of the data to identify higher-order spike correlations and their temporal
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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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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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wastewater using polymeric adsorbers Supervisor: Prof. Stefan Kubik In Process Engineering: B1: Thermodynamic modeling of the recovery of phosphorus from wastewater Supervisor: Prof. Fabian Jirasek B4
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have uploaded your application documents to the DAAD portal, the DAAD will first carry out a formal check. It will be checked whether the application requirements have been met and whether