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Field
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theoretical models and methods as well as in implementing numerical optimization techniques Interest in working closely with experimentalists Detailed knowledge of quantum physics and experience with quantum
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments
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, their modelling through mathematics and numerical simulations, and their control and optimization. Our belief is that a proper understanding of systems requires a modelling step, which allows to identify causal
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
measurements of growth and expression levels of ribosomes and enzymes in the model organism Escherichia coli [5]. The proposed postdoc position involves the numerical simulation, analysis and inference
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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
, and create personal and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our vision is to be
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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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(e.g., superconducting quantum processors). Validate, analyze, and interpret experimental data and results. Simulate the performance of quantum circuits on classical supercomputers. Design and optimize
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. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing