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candidate with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental
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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
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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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Experience M.D. or PhD Required Qualifications Interest in research and scholarship related to migration studies. Previous experience with working with refugees. Immigrants, and immigrant and first-generation
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Are you eager to support exciting research with ultracold atoms and trapped ions? Do you want to participate in the creation of complex machines that have never existed before? Do you enjoy sharing
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] Subject Areas: Physics / Hard Condensed Matter Theory , Machine Learning , Material Science , Physics , Quantum Information Science , Soft Condensed Matter Theory , theoretical condensed matter physics
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following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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commercially orientated research projects in computer vision and machine learning. To be successful you will need: A PhD in Computer Science, Engineering or other Machine Learning-related field. Programming
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experience with machine learning frameworks such as PyTorch or scikit-learn. You are a team player and a team leader: you can mentor PhD students and Bachelor students effectively. You share your knowledge