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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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spectroradiometers. Lead efforts to validate and calibrate hyperspectral data for soil fertility mapping and crop nutrient management assessments. Contribute to the development of spectral libraries and hyperspectral
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. Strong background in reinforcement and imitation learning for robotics. Experience with LLM, VLM or VLA. Proficiency in machine learning frameworks such as PyTorch, and common ML libraries. Experience with
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for robotics. Experience with LLM, VLM or VLA. Proficiency in machine learning frameworks such as PyTorch, and common ML libraries. Experience with ROS and simulators such as Isaac Gym or MuJoCo (this is a plus
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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
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crop nutrient management assessments. Contribute to the development of spectral libraries and hyperspectral imagery for soil fertility mapping and crop monitoring. Disseminate research findings through