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the MSOM journal. • May need to work with external collaborators on numerical experiments with real world data (but this job is purely on academic research and is not part of an industrial collaboration
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Do you have a strong technical background in Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols needed
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Expansion: Implementing the next phase of the project to transition from rigid-body models to sophisticated systems for protein ensemble modeling. Computational Optimization: Resolving hardware-specific
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validating Hydrodynamic models to study performance of integrated floating breakwater and marine renewable energy. Responsibilities include calibrating simulations with experimental/numerical data, performance
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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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and wave-equation–based modeling, including familiarity with adjoint-state methods, gradient-based optimization, and multi-scale inversion strategies. Proven expertise in machine learning and deep
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design and execute experiments using lung cancer and lung infection Organ Chips. Develop, optimize, and characterize human lung microphysiological models for translational studies. Analyze and interpret
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design and execute experiments using lung cancer and lung infection Organ Chips. Develop, optimize, and characterize human lung microphysiological models for translational studies. Analyze and interpret
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magnetic response. Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed