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Develop algorithms for reconstruction of virtual histopathology images from sparse phase-contrast CT acquisitions of oncological specimens Comparison with standard histology and assessment of algorithm
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100%, Zurich, fixed-term The Algorithms and Visualization Group in the Social Networks Lab at ETH Zürich is headed by Prof. Dr. Ulrik Brandes. We develop network data science methods
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open questions in the field live. Research directions include: Using AI to learn parameters and design circuits for quantum optimization algorithms (e.g., beyond QUBO formulations for QAOA) Developing AI
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directions include: Using AI to learn parameters and design circuits for quantum optimization algorithms (e.g., beyond QUBO formulations for QAOA) Developing AI-driven methods to discover quantum optimization
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training Generate and validate synthetic or gym training tasks Run ablation studies comparing algorithms, reward functions, data mixtures, hyperparameters, and infrastructure settings Evaluate model
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their plan, which is based on predetermined routes, lines, and scheduled times, with little possibility of adjusting to unplanned and unexpected circumstances. On the other hand, delays and disruption
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their plan, which is based on predetermined routes, lines, and scheduled times, with little possibility of adjusting to unplanned and unexpected circumstances. On the other hand, delays and disruption