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Demonstrated proficiency with Python, and machine learning libraries; pytorch, sklearn Experience with transformer architectures, and interpretable AI methods and libraries, concept-bottleneck architectures
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. PyTorch, Jax, scikit-learn) applied to genomic datasets Experience with various sequence modeling architectures and interpretable AI methods (attribution methods including SHAP, Integrated Gradients, etc
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architectures, and interpretable AI methods and libraries, concept-bottleneck architectures, attribution packages: shap, captum, etc. Experience with bulk and/or single-cell omics data analysis (e.g. bulk
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