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and image data processing. Specific knowledge related to neural network design, training, and optimization is required. You will be joining a group with core expertise in sensor data analytics from
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science, applied mathematics, physics or a similar area very good programming skills in Python good prior experience with neural networks using common Python-ML libraries such as PyTorch preferably also background
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in English Solid knowledge in finite element analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks
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analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks on material design by using PyTorch or Matlab PLEASE
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, Interactive and Cognitive Systems, Distributed Systems, Parallel Computing, and Networks. The host team, DAISY, is a joint CNRS, Grenoble INP, and UGA research team handling research challenges
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of the finite element method (modelling assumptions, boundary conditions, mesh/element choices, convergence checks). Experience with at least one FE tool such as Abaqus or similar. Scientific programming skills
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is embedded in an international academic–industrial collaboration and targets fundamental questions in end-to-end autonomous driving and neural view synthesis. Your work is expected to lead to
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, segmentation), physics-informed neural networks, and having worked with large datasets are assets. An innovative spirit and team player skills round off your profile. We offer We are a dynamic and international
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potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network potentials (ENNPs) to model nanoparticle energy surfaces. Building atom
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, computer science, applied mathematics, physics or a similar area - very good programming skills in Python - good prior experience with neural networks using common Python-ML libraries such as PyTorch