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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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, Pharmacogenomics, and AI/Machine Learning Job Description: A Postdoctoral Research Associate position is immediately available for a highly motivated, independent, and ambitious candidate in the Department
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at the Department of Biosystems Science and Engineering (D-BSSE) of the ETH Zurich in Basel invites exceptional candidates to apply for a postdoctoral position in pioneering projects developing synthetic biology
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-Directors of the NORD RDCOE. As the center has a strong focus on clinical care and access for individuals with rare diseases, we are seeking a postdoctoral fellow to contribute to the mission to expand
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technologies. The candidate will work at the intersection of Artificial Intelligence, Scientific Machine Learning, and Energy Materials, contributing to the design of self-driving laboratories and no-code
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supports interdisciplinary research at the intersection of artificial intelligence (AI), machine learning (ML), high-performance computing (HPC), and applied scholarship. Reporting to the Provost Fellow for
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Boston, Cambridge, Massachusetts, MA, Northeast, New England, statistics and AI; probability and AI; statistical foundations and methodologies of AI; machine learning; generative AI; trustworthy AI; AI
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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well as introductory nutrition courses. The successful candidate will teach introductory biology courses and upper-level courses in their area of specialty. The successful candidate will also provide a proposal
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data in near-term era quantum computers. Applicants with backgrounds in quantum information or particle physics are both encouraged to apply. Candidates with strong expertise in machine learning, quantum