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• Execute large-scale simulations on CPU and GPU-based HPC clusters • Analyze results, generate technical reports, and deliver project outcomes on schedule • Prepare scientific reports and publish in
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adaptive optimization during needle insertion, integrating live ultrasound imaging with GPU-accelerated dose calculation and optimization. The Postdoctoral Research Associate will join a multidisciplinary
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computing environments, and GPU programming. Necessary skills include knowledge of data processing using software (e.g., Matlab, R, IDL) and/or statistical/mathematical programming languages (e.g., R, Matlab
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and GPU-accelerated tools for circuit and system design optimization, addressing challenges in physical design, timing analysis, and large-scale hardware design automation. The researcher will
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simulation methods, GPU-accelerated computations, several programming languages, and presenting results to wide technical and non-technical audiences. Additionally, the candidate will also develop theory and
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programming (Shared and Distributed memory, GPU programming etc.) Demonstrated experience with distributed memory MPI programming Experience with collaborative software design, development, and testing
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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with OFDM modulation required. Skills Programming skills in MATLAB and or Python required, experience with wireless testbeds desirable, some familiarity with GPU programming desirable (to support
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/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g., concept bottlenecks, prototype