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Field
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. Computational Optimization: Resolving hardware-specific performance and numerical precision challenges across diverse GPU environments. Architecture Design: Leading the design of new loss functions, model
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Detail Information Position Summary The Grant Lab at the University at Buffalo is seeking a highly skilled Postdoctoral Fellowto lead the computational development of a novel generative AI framework
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of three active research communities: operating systems memory management (NUMA policy, page migration, swap behaviour), high-performance and GPU computing (memory coalescing, unified memory, PCIe transfer
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Ryzen CPU, GPU, and NPU, in terms of inference speed, energy consumption, accuracy, and performance per watt. Different quantization levels (e.g., int8, fp16) will also be explored. Develop intelligent
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studies. These pipelines must be capable of efficiently exploiting different types of parallelism, both at the level of a computing node (CPU and GPU) and at the level of a cluster of PCs. This environment
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and GPU-accelerated computing. Familiarity with job scheduling (e.g. SLURM), conda/mamba environment management, and pipeline orchestration (e.g. Snakemake) is required. have strong proficiency in
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, procurement, and deployment of High-Performance Computing (HPC/GPU) clusters and high-capacity data storage infrastructures in strict compliance with Spanish and EU public procurement regulations. Data
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The Geospatial Data Analytics (GDA) Lab is establishing a specialized research position focused on high-performance computing, artificial intelligence, and computer vision. Unlike traditional engineering research
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May 2026 - 00:00 (UTC) Country United Arab Emirates Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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-throughput modeling over heterogeneous or sparse datasets. Conduct system profiling, model benchmarking, and empirical evaluation across different computing architectures. Explore novel strategies