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: N/A Department: Elec, Computer & Bio Engr Campus Location: Job Description Summary: The search will remain open until the position has been filled
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emulators for accelerated forward modeling Advanced data-intensive machine learning and AI techniques for survey analysis Applications to major international surveys, including LSST (Rubin Observatory
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, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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for MND which could be translated into the clinic. The idea is to use cutting edge machine learning to create clinically actionable predictions such as the time from diagnosis to requirement for a
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Website: https://engineering.uark.edu/ Summary of Job Duties: The Postdoctoral Fellow - Biomedical Engineering will continue investigation of cancer nerve crosstalk in both breast cancer and pancreatic
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California State University, San Bernardino | San Bernardino, California | United States | about 1 month ago
, computational, or data-driven approaches-are encouraged to apply. Interest or experience in Artificial Intelligence, Machine Learning, or Big Data Analysis is preferred. Responsibilities include research (85
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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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Engineering, or related field. Research experience with Artificial Intelligence/Machine Learning/Large Language Model. Publication track record in a series of top tier conference papers e..g, in NeuRIPS, ICLR