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Cultural Studies, History, or related field Demonstrated expertise with large language models (fine-tuning, prompting, deployment) Strong Python programming with deep learning frameworks (PyTorch, TensorFlow
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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in Ithaca, NY with a focus on developing deep learning algorithms. Dr. Haiyuan Yu, Ph.D. is a Tisch University Professor of Computational Biology in the College of Agriculture and Life Sciences and a
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning
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demonstrated by publications in international venues in machine learning, AI for science, graph learning or related areas Solid expertise in deep learning, with experience in at least one of the following
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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environment to study these topics given its expertise in Machine and Deep Learning, Computer Vision, Signal Processing, and Multimedia. Also, its declared vision to work especially in presence of imperfect data
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materials property predictions. A deep understanding of materials properties and close connections in academia and industry enable the group to explore exciting research avenues. For more information about
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. Candidates whose doctoral work focused on deep learning methods and who have a strong interest in genomics will also be considered. Experience: At least one publication in computational genomics or machine
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning