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Field
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Biomedical Engineering, Medical Physics, Electrical, Computer Engineering or a
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Electrophysiological signal processing of, e.g., EEG, ECG, EMG, etc. Health data science, incl. modern machine, and deep learning methods, Cloud-based platforms like MS Azure or Google Colab Health data standards, like
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-cell genomics, transcriptome imaging, optical electrophysiology, and machine learning to study how the genome builds a brain across spatial and temporal scales. Key questions we aim to address include: 1
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model APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction
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, skills and abilities: -Proficiency in image analysis using machine learning methods. Continued employment in this position is contingent upon availability of funds. Please attach the following to completed
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fields include (but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. • Experience in deep learning, machine learning and medical imaging processing
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
of microscopy and imaging techniques is a plus. Willingness to learn new in vivo and in vitro techniques, such as performing animal surgeries, intra-muscular, intra-articular and intravenous injections
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, research, and public service. Job Description Purpose: The Department of Electrical and Computer Engineering and AggieFab Nanofabrication Facility at Texas A&M University seeks a Postdoctoral Researcher to
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Posting Details Posting Details Posting Number 0801675 Classification Title Postdoctoral Research Associate- Autonomous Systems and Machine Learning Working Title Department College
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optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques to quell opto-mechanical