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Field
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, the successful candidate will conduct cutting-edge research in: Adaptive incentive mechanism design for complex sociotechnical systems Strategic learning and equilibrium-seeking algorithms in transportation
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-luminosity phase of the LHC. The successful candidate will work in close collaboration with other members of the Particle Physics team, and with members of the Computing, Algorithms and Data team at L2IT
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classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early
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Interatomic Potentials) code for ternary compounds with variable composition with crystal structure optimization algorithms (evolutionary, random, etc.); - Application of the CSP DFT/MLIP methodology to various
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
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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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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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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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develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical