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and biostatistics, with expertise in mathematical and statistical modeling techniques including agent-based models, compartmental models, and network analysis. Previous experience in investigating
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Modeling naturally fractured reservoirs is re-gaining interest in the Oil & Gas industry and academia for application in carbonate fractured reservoirs and unconventional reservoirs where natural
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seeking to expand research, higher education and innovations in the field of Marine Data Modelling and Integration. Applications for a faculty position at the rank of: Assistant Professor are invited from
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: In-situ testing facilities and characterization techniques (Generation of unique databases on well-identified frames), validated models in operational conditions (New models based on real in-situ
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. Responsibilities Development and optimization of perovskite-based solar cells at different levels. Developing large-area perovskite solar cells utilizing KPV-LAB's baseline processes. Performing accurate device
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similar generative models for imaging applications Knowledge of imaging physics and CT reconstruction principles Experience with cloud-based ML platforms (AWS SageMaker, Google Cloud AI) Familiarity with
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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is to develop a modeling framework including the use of Random-Walk method to predict NMR measurements, pore-scale finite-element modeling on 3D digital models, generated from CT-images to predict
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on the development of new methods integrating a variety of data types (remote sensing, geology, geophysics, geochemistry) for geological modelling and advanced exploration targeting of mineral deposits
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and algorithms for efficient learning of dynamical models of micro-states evolving over time including machine based learning of derivation of coarse grained representations; (ii) implementation