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computer or while presenting to individuals or groups. o Frequently required to move about throughout the Medical Center or while rounding. o Constantly required to communicate with people electronically
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Investigate instance and panoptic segmentation for endosymbionts and track them over time Implement, train and test novel machine-learning-based solutions on top-tier super-computing hardware Work in an
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technology, computer hardware, and software ? Answers questions and assists with troubleshooting and resolving technical issues related to printers, copiers, and scanners ? Assists patrons in person, via
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middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g
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it comes to running experiments an affinity to AI technology (incl. machine learning, computer vision) (German) permit to perform animal experiments (equivalent to FELASA B or C) desirable previous
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program to pursue higher education Generous retirement plans with university match Learn more about working at UNL: https://go.unl.edu/aboutus As an EO/AA employer, the University of Nebraska considers
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related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with transformer architectures (e.g., ViT
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for this classification at the discretion of the supervisor. Equipment Operated: Computer and all other standard office equipment. Work Schedule: Typically, M-T-W-Th-F 7:30 a.m. to 4:00 p.m. Unusual Work Conditions: Works
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that are within the level of responsibility for this classification at the discretion of the supervisor. Equipment Operated: Computer and all other standard office equipment. Work Schedule: Typically, Monday
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that may begin monthly or quarterly depending on the course and program. Courses are eight weeks in length and utilize the Blackboard Learning Management System. Each course week runs Wednesday through