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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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in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch). Experience with bioinformatics tools and genomic data analysis. Strong programming and data analysis skills. Excellent
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libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications and machine learning. Integrate and benchmark the GINGKO library, a sparse solver
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(especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. Knowledge of complex systems
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language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch). Experience with