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integrated circuits (IC) and printed circuit boards (PCB). Additionally, the candidate should demonstrate expertise in applying computer vision, image analysis techniques, machine learning, deep learning to IC
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, reinforcement learning, and/or wireless communications. The candidate should hold a doctoral degree in electrical or computer engineering or related fields. The successful candidate will have a strong publication
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d) excellent written, oral communication skills e) strong data analysis skills. Ideal applicants will also have experience with some combination of: a) Machine learning e) code optimization and
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materials such as the magnetoelectric, high entropy oxides, through neutron scattering experiments. Additionally, collaborative work will be performed with the aim of developing and applying machine learning
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for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr
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++, and Python. Preferred Qualifications Prior research experience in computer systems and distributed computing is preferred. However, strong research experience in developing/applying machine learning
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community health workers (CHWs) with advanced data-driven tools to improve healthcare access and outcomes for high-risk patients. The postdoctoral researcher will play a key role in translating machine
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Fluorescence, FRET, & single‑molecule microscopy Mechanobiology, force-spectroscopy Bioconjugation chemistry, nucleic‑acid chemistry Quantitative image analysis, MATLAB/Python scripting, or machine learning
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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, preparing graphs, tables, and charts, etc. Conducts literature searches to develop insights into the significance of experimental results and to acquire information on new or improved methods, procedures and