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Field
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regional aquifers. It aims to approximate full-physics simulations and update predictions using observational pressure and seismic data. The approach combines physical modelling and machine learning, using
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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UiO/Anders Lien 30th September 2025 Languages English English English Postdoctoral Research Fellow in Machine Learning Apply for this job See advertisement About the position Position as
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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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, prediction, and decision support and automation. Responsibilities: The two (2) Postdoctoral Research Fellows will focus on building a digital twin for national-scale field-level crop monitoring, modeling
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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
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, brain science, and neuroscience); data science and machine learning; modeling, analysis, simulation and prediction for biological, engineering, physical and quantum systems; probability theory and
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placed at Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models