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studying deformation mechanisms in refractory alloys as via atomic-scale calculations as well as application of machine learning to materials discovery The ideal candidate will have the following
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SEM, LCA, latent variable models, time-series analysis, multi-level models, power analysis, and/or multiple imputation for missing data. Knowledge of machine learning methods, tools, and software
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and data analysis methods Creation of systems for managing, analyzing, and visualizing large datasets Application of machine learning and AI techniques to domain-specific challenges Collaboration with
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, computer science, or related fields. Demonstrated hands-on experience with machine learning techniques. Strong programming skills (Python preferred). Experience analyzing time-series data, preferably from
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over 2000+ students across its BS, MS, and PhD programs. The department currently has a strong emphasis on research in fundamental and applied computer science including AI, machine learning, computer
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collaborative relationships to ensure that the overall project is progressing together and on schedule. Developing and maintaining machine learning software infrastructure for experiments, and coordinating
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or professional schools. The Department strives to develop abilities and values to serve our regional, national, international, and military communities and create a nurturing learning environment for students
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machine learning software infrastructure for experiments, and coordinating software development across the team. Ensuring electrical safety of the experimental equipment and regulatory compliance
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of Biostatistics at University of Florida and Dr. Hongkai Ji (remote) in the Department of Biostatistics at John Hopkins University. This position, available immediately, focuses on developing statistical, machine
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assimilation, and machine learning frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well