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Field
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field theory, semi-classical methods in quantum many body dynamics, tensor networks and GPU-accelerated quantum evolution. Our work is concept- rather than method-centric. Candidates with backgrounds
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-performance computing, GPU acceleration, automated benchmarking or large-scale numerical workflows. 4. Evidence of self-motivated contributing to collaborative research outputs, open-source software, preprints
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programming skills in Python, MATLAB, C++, or similar scientific computing environments, including high-performance and GPU computing. A strong scientific track record, evidenced by high-quality publications
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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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GPU-centric communication techniques. Collaboration & impact: We actively encourage you to publish your research in high-profile international venues, providing the opportunity to contribute directly to
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system diagnostics. Experience with CUDA and deployment on NVIDIA edge-GPU platforms (Jetson Orin, Thor, or Spark). Experience calibrating and synchronizing LiDAR, IMU, camera, and wheel-odometry systems
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preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing methods. Integrate and analyze large
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AI, …) and relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and
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relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and experience in
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networks, RNNs, LLMs) or in the deployment of such algorithms. Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators