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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 18 hours ago
; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 3 hours ago
project. The position will provide core expertise in physics-based environmental and ocean modeling, with emphasis on numerical simulation and prediction of ocean conditions such as currents, tides, waves
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-learning model. Expected outputs include parameter priors, benchmarking results, a simulation pipeline, curated synthetic datasets, and Bayes optimal errors. Publication / conference: Depending on progress
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-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB, R, or related computational environments. Lead and co-author
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an innovative technology through the complete development process: Physical modelling → CFD simulation → prototype development → experimental validation → scientific publications and technological innovation
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Machine Learning group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models and robust machine learning, together
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position within a Research Infrastructure? No Offer Description We are looking for highly motivated Postdoctoral Researchers in Battery Modelling and Multiphysics Simulation to develop a modelling framework
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combustion and engine simulation, and AI-assisted modelling approaches for hybrid marine propulsion system development incorporating a range of decarbonisation technologies. This position is part of the EPSRC
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on the numerical and mathematical modelling of these complex atmospheric phenomena. This is a multifaceted research role that draws on mathematical modelling, high-fidelity numerical simulation, and close
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extreme weather forecasting, employing data science models as developed in computer vision and natural language processing (NLP). This project leverages the large-scale high-performance computing (HPC