2,803 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in United States
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. Qualifications Required Qualifications: Completed PhD in biomedical engineering, electrical engineering, physics, or a medical imaging related field. Experience with developing advanced pulse sequences
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The Physics Department is deeply involved in the development of the Electron-Ion Collider (EIC), a next-generation facility that will advance the long-term vision for nuclear physics. In December
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benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; access to UA recreation and
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Zheng, and other collaborators to develop statistical and machine learning methods for medical decision-making in cancer prevention and early detection. Key areas of focus include, but are not limited
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
Postdoctoral Trainee - Carolina Cancer Nanotechnology Training Program (Postdoctoral Scholar) Vacancy ID PDS004223 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC
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PhD in STEM Field (including computational or mathematical social science). ? Ability to program in C and Python. ? Must know basic statistics. ? Experience in social network
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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and lifting object from floor to above shoulders up to 25 pounds. Standing and sitting at computer terminals and laboratory bench completing repetitive tasks for extensive periods of time. Continuous
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are also developing novel machine learning methods to improve risk gene prediction and variant interpretation. This role will focus on the analysis of large-scale human genetics, scRNAseq, and proteomics