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nanocomposite and supramolecular materials for next-generation integrated sensor arrays. This role supports innovation in intelligent sensing technologies, contributing to the development of scalable
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of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas: image processing, machine learning, and patient records. Track
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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive
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(terrestrial and NTN). The goal of this research is to design and develop algorithms and techniques that adapt to the environment, minimizing signaling overhead associated with channel estimation and enhancing
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be amended from time to time. Federal, state, or local government directives may impose additional requirements. The University of California is an Equal Opportunity Employer. All qualified applicants
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Position Description https://universityofcalifornia.marketpayjobs.com/ShowJob.aspx?EntityID=38&JDName=Associate%20Physician%20(40969678) Create a Job Match for Similar Jobs About University of California Los
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/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
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; • Proficiency in programming; • Proficiency in additive manufacturing-related software, including 3D modelling and path planning; • Ability to integrate sensors (e.g. laser scanning, thermal imaging) for
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Postdoctoral Research Fellow to work on the design, development, and realization of future communications technologies. You will be part of the team and contribute to ongoing developments in theory, algorithms
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Eligible: Yes This position is 100% on-site Benefits Outstanding benefits and perks are among the many rewards of working for the University of California. UC Davis offers a full range of benefits, resources