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Field
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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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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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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
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, or GPU programming is an asset LanguagesFRENCHLevelGood LanguagesENGLISHLevelGood Research FieldEngineering » Electronic engineering Additional Information Selection process Applications must be sent
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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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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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, IEEE IV and RadarConf. For your research, you will have access to extensive computing resources at TU Delft, ranging from personal workstations and shared GPU servers to the Delft AI Cluster and the
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shared GPU servers to the Delft AI Cluster and the DelftBlue supercomputer. Your supervisors will be Prof. Dariu Gavrila and Dr. Julian Kooij. Job requirements We are looking for a candidate with: An MSc
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structures is essential. Experience with finite element, finite difference or discrete element modelling (e.g., FLAC3D, 3DEC, Abaqus, COMSOL or similar software) is considered an advantage. The successful
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architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses Research experience (e.g., through a Master thesis work or research internships) is