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an extensive safety analysis and calidation of perception algorithms in automotive. Through our work, we lay the foundation for a reliable digital future. What you will do Quantum computing is an emerging
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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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to their spin properties. What you will do You will program the connection of quantum experiments to an existing HPC system for the external execution of quantum algorithms. You will develop an efficient user
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neuromorphic hardware including novel materials, devices and circuits. Implement bio-inspired learning algorithms on said hardware. Collaborate with an international, multi-disciplinary team to achieve our
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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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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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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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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