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pursue the use of machine learning techniques for data analysis. Candidates must have a Ph.D. and research experience in experimental high energy physics. The successful candidate is expected to carry out
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, bioinformatics, or other related disciplines is required. Strong interest, research background and experience in the methodology research in statistical genomics, machine/deep learning, bioinformatics methods in
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Qualifications Experience with machine learning and deep learning Ability to analyze and interpret complex geophysical data and apply appropriate research methodologies Additional Information: The College of Arts
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at Carnegie Mellon University invites applications for a postdoctoral researcher to lead projects at the interface of computational chemistry, machine learning, reaction mechanism elucidation, and automated
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origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http
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network inference and modeling, machine learning and deep learning. Experience in working with Arabidopsis and plant genome data is a strong plus. The position is expected to continue for multiple years
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developing new ones, including machine and deep learning methods. There will be opportunities for development of new cell line and animal models and testing, evaluation, and analysis of new genomic
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Learning (ML), Deep Learning (DL) particularly in Natural Language Processing (NLP) and Computer Vision (CV) - strong record of publications - Excellent communication skills and ability to work in a fast
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monitoring systems, bedside monitoring devices, or medical device data. Experience linking physiologic waveform features to clinical outcomes. Experience with machine learning, deep learning, predictive
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or deep learning reconstructions). Knowledge of radial data acquisition strategies, artifact mitigation methods, and their use in parametric imaging (e.g., T1/T2/T2* mapping). Preferred Qualifications