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-throughput processing. Enhance the computational efficiency of compute environments by optimizing resource allocation (including CPU/GPU utilization), parallelizing data pipelines, and resolving processing
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. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state-of-the-art IT infrastructure. Ideal
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, Statistics, Biostatistics, Bioinformatics, Computational Biology, or a related quantitative field. Strong programming skills. Experience with machine learning frameworks such as PyTorch or TensorFlow
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