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Field
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experience in the development of first-principle wave models. Recent research experience in the development of numerical codes for complex electromagnetic scattering scenarios, and the use of full-wave
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and magnetic data to map subsurface structures 3. Basin modelling, with knowledge of sedimentary processes and tectonic evolution The project would contribute to mapping the thickness of sedimentary
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. Expertise in artificial intelligence and machine learning. Recent research experience in the development of first-principle wave models. Recent research experience in the development of numerical codes
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: Development and calibration of numerical models of pilot networks to explore the use of multiple monitored parameters (pressure, flow rate, basic and advanced water quality parameters) for anomaly detection
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deep expertise in AI/ML who are motivated to move beyond benchmark-driven modeling and into biologically grounded, experimentally validated, and translationally impactful work. Fellows will operate
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the opportunity of a 2-year extension) Āu Mahi | What You Will Do As a Postdoctoral Research Fellow in CEE you will apply specialist research-related skills in numerical reservoir modelling, develop new knowledge
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; Additional qualifications (required at time of start) • Experience in numerical fluid dynamics or ocean model application, validation and analysis; • Ability to collaborate effectively in a team environment
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and granular substrates. In this role, you will perform numerical simulation and reduced-order modeling of naturally-shaped granular materials interacting with specific foot structures. You will also
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neurophysiological experiments - mathematical analysis of the dynamics of neural networks - programming and numerical simulations of neural networks - development of quantitative model predictions and
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Desired qualifications: Experience in developing and modifying theoretical models of scattering data, including analytical (form- and structure factor) models, discrete numerical models and model