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. You must hold a master’s degree in Electrical Engineering, Computer Science, Mathematics, or similar. The work is interdisciplinary and we will closely collaborate with a group at the chemistry
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microfluidic techniques and 3D cell culture, you will grow human microvascular tissue on chip whose architecture self-organizes in response to vasoactive substances. Your work will lead the way to applications
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environment using machine learning technologies. This PhD position is part of our research on exploiting social media data for earth observation tasks. The work will be on the topic of developing geographically
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg
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. The position is hosted at the Chair for Algorithms and Complexity, headed by Prof. Susanne Albers (http://wwwalbers.in.tum.de/index.html.en). The dissertation work will involve research in the fields
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transported nutrients. Combining micro-fluidic techniques and 3D cell culture, you will grow human microvascular tissue on chip whose morphology self-organizes in response to vasoactive sub-stances. Your work
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collaboration with partners from science and industry. You will experimentally investigate novel quantum sensing paradigms in illumination- type experiments with propagating quantum microwaves. Your work is
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learning to push our understanding of the robustness and explainability of Federated Learning models. Your responsibilities: Build and create clinical use-cases for benchmarking existing state-of-the-art
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clinical use-cases for benchmarking existing state-of-the-art (SOTA) Federated Learning algorithms. This includes running a few pre-processing pipelines. Develop SOTA FL algorithms that tackle data
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on realtime operation and ensuring user privacy across all operations. This thesis will be carried out in tandem with a PhD student in EE working on energy efficiency and sustainability as well as real-time