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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
strong machine learning and numerical modelling background to add knowledge on the impact of geological heterogeneity and subsurface environments (e.g., depth, exhumation, temperature, pressure) to de-risk
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mathematics, Earth science, or a related discipline Skills in numerical modelling, programming, and handling large datasets Prior experience in machine learning is desirable Interest in nonlinear dynamics and
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programming, algorithm development and deep learning model implementation, and practical experience in drone and boat-based surveys are preferred. Background Investigation Statement: Prior to hiring, the final
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machine learning models and neural circuits in the brain process states of emotion. The Nair group combines cutting-edge systems neuroscience tools with the development of new AI methods to understand and
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learning, Large Language Models, Stochastic optimization, Transfer & Evolutionary optimization, Bayesian optimization for complex design in material and engineering. Key Responsibilities: Collect relevant
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, United States of America [map ] Subject Areas: Electrical and Computer Engineering / artificial intelligence , Artificial Intelligence and Machine Learning (AI/ML) Starting Date: 2026/01/01 Salary Range: $62,232-$80,000
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Full-time: 35 hours per week Fixed term: 6 months Develop machine learning of cellular automata models with applications to in vitro developmental biology. The Opportunity: This post is full-time
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assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting
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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault diagnosis, and early fault prediction in electric vessels