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proteins, functionalities and application in food products. Experience and willingness to learn Proteomics and/or flavoramics, chromatography, and spectroscopy methods, instruments, and software are required
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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Bioinformatics, as well as the Departments of Biostatistics & Biomedical Engineering, University of Michigan is seeking a postdoctoral fellow for bioinformatics problems involving quantum machine learning and
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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candidate will have strong analytical skills and substantial experience in machine learning at scale. The Prorok Lab in the Dept. of Computer Science & Technology, has a variety of robotic platforms (aerial
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postdoctoral Research Fellow (RF) position for one year with a possible extension for one more year. The starting date is November or December 2025. This post will advance the application of Machine Learning (ML
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, Recommender Systems, Machine Learning or Artificial Intelligence (applied to text or financial data) A3 A comprehensive and up-to-date knowledge of the wider subject area or subject specialism in Information
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning