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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
Demonstrated research experience in machine learning, deep learning, medical image analysis, computer vision, biomedical data science, or a closely related area. Strong programming skills in Python and
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, Python or R Experience in manipulating and analyzing large observation data or model outputs Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes Strong communication skills
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statistical computing in R and/or Python. Abilities: The following capabilities are desired: independent research capabilities, analytical acumen, attention to detail, initiative, and ethical conduct
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 12 hours ago
Demonstrated research experience in machine learning, deep learning, medical image analysis, computer vision, biomedical data science, or a closely related area. Strong programming skills in Python and
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programming and simulation tools, such as Python, C/C++, MATLAB/Simulink, ROS/ROS 2, Gazebo, AirSim, or equivalent platforms. Experience in the experimental implementation and validation of robotic systems
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audiences. Proficiency with standard office and data tools (Microsoft Word, Excel, PowerPoint, coding (R or Python), reference managers). Experience demonstrating solid judgment, organizational, planning
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requirements and priorities. Strong skills in data analysis, image processing, and programming (e.g., Python/C++). Demonstrated experience in publishing scientific journal papers. Fluent in English (oral and
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biological or genomic datasets. Proficiency in programming languages commonly used in scientific computing (e.g., R, Python, Linux/Unix environment). Excellent analytical, written, and verbal communication
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data analysis (e.g., MRI preprocessing, statistical modeling, familiarity with tools such as FSL, SPM, AFNI, or similar platforms). Proficiency in statistical programming (e.g., R, Python, MATLAB
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-effectiveness of strategies for preventing long-term central venous catheter-related infections in oncology services. Responsibilities include data analysis using Python and R, health economic evaluations