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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned
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Engineering, or related field. Research experience with Artificial Intelligence/Machine Learning/Large Language Model. Publication track record in a series of top tier conference papers e..g, in NeuRIPS, ICLR
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for collaboration. You will also have the opportunity to develop your own research project aligned to the interests of the MND group. This could include new machine learning models or exploring a particular aspect of
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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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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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manufacturing principles. Experience with machine learning methods and integration into hybrid modelling systems Demonstrated ability to clearly communicate research concepts and results in high-quality journal
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profiling, and other cutting-edge, high-dimensional tissue analysis approaches to evaluate pancreatic cancer pathology using human tissue specimens Assemble analysis pipelines using machine learning
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural 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