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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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algorithms in extremely complex and enormously large spaces motivated by physics and chemistry Developing interpretable AI for scientific discovery in physics (example here ) Formal mathematics (using Lean’s
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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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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appearance changes due to weather and season these algorithms can lead to wrong associations and degraded performances. The classification and segmentation of the point cloud information can be a major
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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with the latest sensors (camera and LiDAR sensors), is available for the work. What you will do – development of algorithms for 3D multi-object tracking based on heterogeneous sensor data fusion
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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) software for topology-informed biomedical image analysis and large foundation models. You will be responsible for Develop new machine learning algorithms for microscopy image analysis problems (2D/3D/4D/5D