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development of analytical solutions, data analysis and machine learning. Candidates should have a demonstrated record of scientific publications in international journals and participation in conferences
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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represented by the United Auto Workers of America (UAW) and information about the collective bargaining agreement is available on the UCOP website. https://ucnet.universityofcalifornia.edu/labor/bargaining
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employer, and in keeping with this commitment, we welcome all to apply, including veterans and persons with disabilities. Job Description: Budget Title: Postdoctoral Associate (SL-1) Salary: $80,000 (full
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employer, and in keeping with this commitment, we welcome all to apply, including veterans and persons with disabilities. Job Description: Budget Title: Postdoctoral Associate (SL-1) Salary: $80,000 (full
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annotation, and emerging machine-learning and generative methods for spectra or structure proposals. Evaluate and test emerging technologies (hardware and software) in close interaction with collaborators and
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at New York University More Jobs from This Employer https://main.hercjobs.org/jobs/22002419/postdoctoral-associate-x28-rocha-lab-x29 Return to Search Results
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assessment and clinical outcomes across diverse populations. Applying artificial intelligence, machine learning, or advanced analytic methods to behavioral health data sources, including mobile health data
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Machine Learning, Computer Science, Mathematics, Statistics, Physics or a closely related field and want to join the mission of unlocking the “geometry of artificial intelligence” then come join us! Join us
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
from all backgrounds to join our community. The Nonlinear Systems and Control group is seeking a talented and ambitious Postdoctoral Researcher to develop machine learning-enabled approaches