8 machine-learning research jobs at University of North Carolina at Charlotte in united-States

Postdoctoral Fellow-Center for Precision Metrology

limited to 5-axis milling, low speed machining dynamics, free form optics, physics informed machine learning (PIML), robotic machining, cutting tool design, hybrid manufacturing context. - Draft Conference

Postdoctoral Fellow-Electrical/Mechanical Engineering

in battery management systems Experience with software tools and libraries for machine learning (e.g., TensorFlow, TensorFlow-Lite, PyTorch, Scikit-learn) Excellent analytical, technical, and problem

Postdoctoral Fellow-Computational Materials Science

professional apprenticeship designed to provide recent Ph.D. recipients with an opportunity to develop further the research skills acquired in their doctoral programs or to learn new research techniques, in

Postdoctoral Researcher

following areas: data management, survey methodology, multilevel regression, causal inference, machine learning, geospatial analysis. Preference is also given to applicants with research interests in one

Postdoctoral Fellow; Electrical and Computer Engineering

imaging is preferred. Background in signal and image processing, machine learning, deep learning, image reconstruction will be helpful. 4. Proficiency in programming languages (e.g., MATLAB). 5. Strong

Postdoctoral Fellow; Biological Sciences

an opportunity to develop further the research skills acquired in their doctoral programs or to learn new research techniques, in preparation for an academic or research career. In the process of further

Postdoctoral Researcher; Political Science and Public Administration

management, survey methodology, multilevel regression, causal inference, machine learning, cross-national research. Preference is also given to applicants with research interests in one or more of the

Postdoctoral Fellow

to attract external funding. The employee’s principal research responsibilities will be performing research on AI and machine learning concepts and other data analysis techniques applied to energy forecasting