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Field
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. Knowledge of systems programming skills and concepts Experience with fundamental OS and storage concepts. Experience with programming heterogenous architecture. Preferred Qualifications: Experience with CUDA
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, evidenced by e.g. projects on Github. Familiarity with deep learning hardware / accelerators and GPU/CUDA kernels.
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and distributed memory programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical algorithms and libraries
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for distributed AI training, agentic AI with modeling and simulation, and end-to-end workflow monitoring, profiling, and optimization. Working with quantum simulation tools, including NVIDIA CUDA-Q, to enable
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Considered Knowledge of CUDA. Knowledge of PyTorch or a similar deep learning framework Experience writing software trigger infrastructure. Experience mentoring more junior researchers Physical Requirements
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and/or optimisation; programming in C/C++, CUDA, etc., and software development and integration will also be valued. I.II - The non-degree-granting course must be related to the type of activity covered
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++ and CUDA) Code verification and validation of simulations Providing simulation results to project partners and customers Active participation in project, group and lab meetings, knowledge transfer
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-motivation skills Strong foundation in CFD with proficiency in Python and AI/ML techniques, and additional experience in parallel computing tools such as CUDA and MPI Experienced with CFD simulation tools (e.g
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shaping and imaging Proficiency in GPU programming (CUDA/OpenCL) and strong coding skills in C/C++ and Python Practical experience with Spatial Light Modulators (SLMs) and wavefront shaping techniques Solid
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, or similar). Experience with parallel programming frameworks (e.g., MPI, OpenMP, CUDA) and high-performance interconnects (e.g., InfiniBand). Preferred Qualifications: Familiarity with advanced storage