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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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new globally accessible infrastructure and new algorithms for multimodal mining of biosynthetic diversity, as well as novel lead compounds that can be taken up by consortium partners to attain concrete
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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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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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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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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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, 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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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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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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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