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Field
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CUDA and scientific computing libraries (e.g., NumPy, SciPy). Workload: Approximately 15 hours per week on average during the semester, with the possibility of increased working hours (up to 40 hours
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in 4D radar, thermal camera, SLAM or robotics software. Strong C++ and CUDA programming, with ROS1/2 experience. Experience in deploying SLAM on Jetson, ARM, or other edge platforms. Familiar with SLAM
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-performance computing (HPC), parallel computing frameworks (such as MPI, OpenMP) and GPU acceleration. Proven experience administering, configuring, and optimising HPC clusters and GPU systems (e.g. CUDA
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computing (Rust, C, C++). Algorithm development for radio astronomy heterogeneous high-performance computing (CUDA, OpenCL) Basic Qualifications Bachelor's degree in EE or Physics. 5 years experience in
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training in the second area are encouraged to highlight this in their application. Experience with high performance computing and GPU acceleration tools (e.g. CUDA) and deep learning frameworks, such as
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knowledge of programming, software development. Proficiency in at least one programming language such as Python, Fortran, C++, or CUDA, with the ability to learn others as needed. Familiarity with tools
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., optimization, dynamical systems, graph theory, probabilistic modeling) or adjacent fields with engineering impact. (4) Fluency in prototyping and software development (e.g., Python, C++, CUDA, or ML frameworks
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Microscaling Data Formats for Deep Learning INT4 Decoding GQA CUDA Optimizations for LLM Inference Pruning and Distillation to Enable Llama 3.2 1B and 3B Models Suitable for Mobile Devices PyTorch Distributed
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analysis and tuning. Excellent C/C++ and Python programming skills. GPU programming would be welcome (CUDA). Required Documents Resume
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, MATLAB, Git, debugging, and modern software engineering practices. Experience with GPU computing (e.g., CUDA, HIP), parallel computing (e.g., MPI, Actor Model). Familiarity with containerization (e.g