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Field
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and hundreds million) Skills in AI-enabled and GPU-based calculations are welcome Good communication skills and the ability to work in a team environment Ability to work independently to solve critical
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research. BOLD also has access to national compute resources (3M GPU hours on Isambard for the first 1.5 years) and we are working hard to get to 5000 H100 equivalent compute capacity in total across
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. Experience with embedded systems and hardware/software integration in a research or prototyping context. Experience with FPGA/RTL development and Xilinx tools, and CUDA coding for GPU acceleration. Knowledge
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systems, FPGA/RTL, and RF technologies, and CUDA coding for GPU acceleration is a plus. Strong leadership, communication, and documentation skills.
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bridging (e.g., JESD204C/eCPRI), and CUDA coding for GPU acceleration. Knowledge of RF calibration, test automation, and signal integrity in a research or prototyping context. Track record of peer-reviewed
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trajectory optimization, nonlinear programming, or genetic algorithms. High-performance computing (HPC), parallel numerical workflows, or GPU-accelerated model execution. Terms of Appointment This is a full
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machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications: Experience with multi-GPU model training and
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, microfluidics, laboratory automation, and GPU computing infrastructure. The opportunity to develop AI methods and scientific software that are directly deployed on cutting-edge experimental platforms. Vacation
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experiences on computer vision • Strong programming skills in using Deep Learning tools like PyTorch and GPU clusters. • Good written and verbal communications. • Open to Fixed Term Contract
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Processing Unit (GPU) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed