2,755 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in United States
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: Physics / Nuclear Theory , quantum information/computation Appl Deadline: none (offers accepted posted 2025/04/09) Position Description: Apply Position Description The Department of Physics
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Program. The research activities will focus on climate-driven input, transport, and fate of plastic debris in the Lake Ontario watershed and nearshore region. Job Responsibilities Applicants must hold a PhD
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processing and analyzing genomic sequencing data is essential. Experience working with computational tools such as R and UNIX is essential. Experience with handling of laboratory animals is preferred and
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physics. Participation in detector research and development, especially for low-latency event selection in trigger systems. Development of new artificial-intelligence and machine-learning techniques
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space-based systems, including large satellite constellations. A recent PhD in physics, engineering, computer science, or other relevant fields and strong interest in technical and policy research
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deployment. Experience with reinforcement learning (RL), computer vision, and sim-to-real transfer. Experience with robotic hardware platforms such as mobile robots, robotic arms, and embedded sensors
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: Education: PhD or PsyD in Clinical or Counseling Psychology, or completion of all degree requirements, including a one-year full-time internship from an APA-accredited program. All degree requirements
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
professionalism, enhance learning, and create personal and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our
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for a post doctoral researcher in computational design and digital fabrication. Reporting to Tsz Yan Ng, associate professor of architecture, you will work on robotic 3D printing processes such as
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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