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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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but not limited to: o Quantum error correction, fault tolerance, and resource optimization. o Entanglement dynamics, quantum control protocols, and hybrid quantum-classical algorithms. o Modeling and
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algorithms for mining molecular multi-omics and medical data to improve multiple tasks of precision medicine and discover new precision therapeutics. The successful candidates will work closely with our
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algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project "Utility Optimization in Quantum Networks: Algorithm Design and Analysis", working with Dr
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opportunity to develop cutting-edge AI-enhanced algorithms for analyzing transposable elements (TEs) in cancer epigenomics, with direct translational applications to precision oncology and immunotherapy
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learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms at the intersection of robot learning, control, and AI
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algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project “Utility Optimization in Quantum Networks: Algorithm Design and Analysis”, working with Dr
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computational models; (ii) optimization and related graph-theoretic methods, using polyhedral geometry and algorithmic optimization (e.g., vertex/facet–based techniques) to formulate and solve core computational
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close