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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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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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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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approaches across a range of model organisms to understand how and why we age. As a PhD candidate at FLI, you’ll be part of an international and interdisciplinary environment where basic science meets
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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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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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interpretable, synthesis-proximal modifications to known materials. Create generative models for material discovery adhering to strict physical constraints needed for stable and synthesizable crystal structures
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carry out a formal check. It will be checked whether the application requirements have been met and whether the application is complete. If this is the case, you will be asked to submit your artistic