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industry partners to advance the lab's research goals. Qualifications: A Ph.D. in Computer Science, Engineering, or a related discipline, with a focus on security, privacy, or applied machine learning, and
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 2 months ago
position focused on advancing crop production and physiology through field experimentation, computation (statistics and machine learning), and process-based models. The successful candidate will have an
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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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PhD (within the last 0-5 years) in field of materials science, chemistry, chemical engineering, computer science, or a related field Experience operating and troubleshooting laboratory automation
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-scale, diverse datasets using machine learning and AI techniques. Findings will be disseminated through peer-reviewed publications, conference presentations, and public engagement. About You You will hold
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computer engineering degrees with previous experience in natural language processing; or computational linguistics/language technologies degrees; (b) strong programming skills and machine learning experience
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well as machine learning and multivariate decoding of neuroimaging data to predict subjective experiences and individual differences. Successful candidates will be supported in building a research program at the
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inference), and automated machine learning. holding (or close to acquiring) a PhD degree in Computer Science, Artificial Intelligence or a closely related field; strong research vision and an academic mindset
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | 2 months ago
. The ideal candidate would have some, but not necessarily all, of the following: 1. A PhD in Chemical Engineering, Material Science, Chemistry or a related discipline; faculty member at an accredited college
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) develop multimodal machine learning models and methods to determine signatures and biomarkers to understand mechanisms distinguishing spontaneous versus precipitated withdrawal episodes. The spontaneous vs