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Alzheimer’s disease to develop and/or apply computational approaches to large scale genomic or transcriptomic datasets for identification of targets for early detection, prevention or treatment of Alzheimer’s
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
pending funding and performance. Minimum Education and Experience Requirements PhD in Computer Science, Computing, Statistics or Data Science, or related disciplines Required Qualifications, Competencies
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly
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. Activities The postdoctoral researcher will be part of a team involved in the data analysis and interpretation of two major very-high-energy gamma-ray observatories: the Large High-Altitude Air Shower Array
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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this position will also possess: Data analysis, software and big computing. Salary: Compensation will be commensurate based on the selected candidate's education and experience. Please note: This position is
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experience in music technology, especially related to the fields of music understanding and music information retrieval Experience in managing large data collections Experience in teaching and supervising
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candidate will focus on implementing efficient and scalable data storage modes for large-scale mass spectrometry (MS) data and on development of improved and interactive visualization of MS data. In addition