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Science, or Information Science, with no more than five years post receipt of the PhD. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data
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design. Perform large-scale computational screening using first-principles calculations and machine-learning potentials. Analyze structure–property relationships and extract scientific insights from AI
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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science, machine learning or a related field. Strong experience working with clinical, biomedical, genetic, electronic health record, or health administrative data. Experience with large language models
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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in internal meetings and at national/international conferences. ● Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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decisions. Adheres to University and unit-level policies and procedures and safeguards University assets. Develop various machine learning and data mining models including deep convolutional neural networks