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to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems
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learning, particularly in deep learning or related areas. No prior knowledge of cryptography is required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications
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on classical supercomputers. Design and optimize classical simulation algorithms for quantum circuits. Explore approximate classical simulation algorithms (e.g., tensor networks, circuit cutting) to improve
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researcher in algorithmic game theory and/or online learning, working with Prof. Celli at BIDSA and the Department of Computing Sciences. The project studies how multiple machine learning algorithms interact
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established AGATE Centre for Algorithmic Game Theory in Socioeconomics. Informal inquiries are welcome and may be sent to loebl@kam.mff.cuni.cz . Where to apply E-mail positions@kam.mff.cuni.cz Requirements
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algorithmic fairness Formulation of new problems and research directions and translating topical issues into algorithmic problems Designing new algorithms and investigating their performance on synthetic and
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degree in the field of bioinformatics and computational biology. Work plan: To develop and evaluate a hybrid quantum–classical approach for NGS data alignment, combining Grover’s algorithm with classical
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classical simulation algorithms for quantum circuits. Explore approximate classical simulation algorithms (e.g., tensor networks, circuit cutting) to improve scalability. Publish scientific papers and present
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invites applications for a Post-Doctoral Associate position, in the area of Quantum Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects
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speed, classifying riding styles (e.g. conservative or more aggressive). Next, using inputs such as terrain type and slope, a decision algorithm based on Fuzzy Logic, decision trees or a lightweight