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++, maintained on github and configured via python. You will work with .root files and explore different event generators as well as machine learning tools and algorithms. The nature of LDMX as an international
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potential of these measurements, there is a strong need of developing AI algorithms able to do real-time analyses of such data. Your immediate leader will be the Group leader of Manufacturing Engineering at
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, design, implement, and validate an optimization system for scheduling group classes within the Koachy platform. The specific goals include: Develop attendance prediction models for different types
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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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of algorithms and digital neuromorphic hardware is an additional avenue for enhancing the efficiency of the methods. In this context the research will explore digital, event-based implementations
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Circuits, Robust and Efficient Mapping of Quantum Algorithms on Quantum Machines, Quantum Noise-Aware Optimizations for QML, QML Security, Error Correction for Quantum Computing, Secure Quantum-Classical
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of the secondary lobes generated with a Voronoi algorithm. Include and optimize the model of lesion dynamics including encapsulation, infection and endogenous reinfection through the bronchial tree. Develop virtual
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and conferences. Proven experience in design and implementation of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas
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on the development, optimization, and clinical evaluation of new x-ray-based imaging methods. The lab focuses on the use of medical physics approaches to improve image acquisition methods and processing algorithms
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Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum experiments and quantum algorithms for computational geometry problems. Prior