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collaboration with local water utilities and software developers Integrate digital urban water twins with data, applying methodologies for data assimilation, parameter estimation, and quantification of model
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to the discussions with the project partners (with reciprocal visits during the employment period) and work towards the project deliverables; explore new methods construct LRC codes from algebraic curves and/or
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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orthopaedic surgery. This role combines medical imaging (ultrasound and MRI), computer-assisted surgical technologies, and the study of how bones and joints move. The targeted starting date is March 1, 2026
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(UCLA) as external collaborator. Brief project description The project develops new methods for causal inference when covariates are high-dimensional – settings in which traditional estimators often face