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Field
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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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Discrete Element Method (DEM) software GranOO. Expected Outcomes The digital twin approach will be validated against experimental results through the comparison of: (i) thermomechanical properties such as
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internal erosion and pipe progression in clay-sand layered dikes. Derive constitutive modelling approaches from high-fidelity DNS-DEM data generated within the broader project consortium. Capture the multi
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: Develop continuum-based two-phase models to predict internal erosion and pipe progression in clay-sand layered dikes. Derive constitutive modelling approaches from high-fidelity DNS-DEM data generated
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trading strategies. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing tens of thousands of high-end GPUs. Depending on the day, we
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ice, using a framework consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ
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compute, for use in labs, factories, farms, homes and inspection settings. Durham offers strong facilities: Bede HPC (128 GPU), GPU cluster (90+ GPU), LiDAR, RADAR, drones, cameras, embedded devices and
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machine learning, computer vision, robotics, efficient inference or embodied AI, prioritising practical, reliable systems. Durham offers strong facilities: Bede HPC (128 GPU), GPU cluster (90+ GPU), LiDAR
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Skills/Qualifications Technical Skills: Advanced programming in Python, PyTorch, PyTorch Geometric or DGL. Version control (git), Linux and model training on GPU (reproducible experiments). Other
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and HPC/GPU systems, with version control (git) and reproducible workflows (conda or containers, Snakemake or Nextflow).•Able to work independently as well as within an interdisciplinary, international