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Field
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languages (e.g. C/Fortran) Shared and distributed memory programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical
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programming paradigms and frameworks like MPI (Message Passing Interface), OpenMP, CUDA, or OpenACC for developing highly parallelized applications. Familiarity with HPC tools and libraries such as SLURM, PBS
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Proficiency in CUDA programming and hardware-accelerated computing Experience with high-performance I/O and efficient data access protocols for distributed scientific computing Expertise in customizing data
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, OpenCV), GPU computing (e.g., CUDA), SLAM and point cloud processing (e.g., PCL). Familiarity with ROS-based development and Linux software tools is considered a fundamental asset. (30
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collaborative mindset, contributing to both individual task success and overall team cohesion. Below are seen as merits Proficiency in CUDA programming and hardware-accelerated computing Experience with high
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written and oral communication skills. Experience with MPI, OpenMP, parallel I/O (including HDF5), CUDA and/or OpenACC, Fortran, C++, Python. Motivated self-starter with the ability to work independently
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preferred: OpenLDAP, Slurm, IBM Spectrum Scale, Ansible, Podman, Singularity/Apptainer, MPI, Jupyter, JupyterHub, NVIDIA CUDA, Infiniband, Spack, Sphinx, and Jenkins, though comprehensive expertise is not
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Operating System (ROS/ROS2). Solid experience with computer vision libraries (OpenCV) and deep learning frameworks (PyTorch, CUDA) A firm grasp of control theory and its practical application in robotic
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programming paradigms and frameworks like MPI (Message Passing Interface), OpenMP, CUDA, or OpenACC for developing highly parallelized applications. Familiarity with HPC tools and libraries such as SLURM, PBS
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programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA