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for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite
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include deep learning, reinforcement learning, differentiable modelling and inverse design. You will implement and evaluate these methods using experimental optical systems and work towards their
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modeling and inversion studies Publish high-impact research articles in leading international journals Assist in the preparation and development of competitive research proposals Support the supervision and
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detection, inversion or imaging, system identification, and data fusion. Integrate DAS with complementary sensing modalities and computational tools, including AI or ML where appropriate, to improve
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to welcome an outstanding candidate to work in the field of ionospheric empirical modelling, D-Region ionospheric remote sensing, inverse methods, or ionospheric radio propagation. Background SERENE is a
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theories in inverse/scattering problems. Once successful, the candidate is expected to transfer their expertise to machine learning/scientific computing in collaborations with other group members (phd
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complex environments. Explore new imaging and sensing approaches that integrate physics-based models with artificial intelligence and conduct research on complex inverse problems and information
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, race, gender, religion, marital status and family responsibilities, or disability. Key Responsibilities: The candidate will study theoretically forward and inverse uncertainty quantification problems
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for generating broadband THz waves through femtosecond laser-induced spin excitation and inverse spin Hall effect. This ultrathin spintronic THz emitter design is hypothesized to prevent drift current between
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. Job Requirements: PhD in Geophysics, Seismology, Civil/Structural Engineering, Engineering Mechanics, Applied Physics, Electrical/Computer Engineering, or a related field. Strong research record in