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such as NumPy, SciPy, PyTorch or TensorFlow Experience with C/C++ and GPU/accelerator platforms is an asset Hands‑on experience with software‑defined radio platforms, RF measurement equipment, or laboratory
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. Experience with high-speed data acquisition, signal processing, or FPGA/GPU-based DSP is considered an advantage. The ability to work independently while contributing effectively to a collaborative research
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, with proficiency in Python and deep learning frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 hour ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large medical datasets e.g., large magnetic resonance
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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tracking), dataset curation, HPC/GPU programming, blockchain for secure data, C-family languages, and embodied AI/robotics are a plus. Experience with general network resilience, cellular automata
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algebra methods targeting large-scale HPC systems. Optimization of linear algebra libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications
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datasets generated by the Phenomobile.v2+ to identify key traits affecting crop performance under stress conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU
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the Northern 8 cluster, members of Durham University can also submit projects to the Tier 2 supercomputer Bede (32 IBM Power 9 dual-CPU nodes, each with 4 NVIDIA V100 GPUs and high performance interconnect