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, collaborate openly across institutions, and have the stamina to push through the engineering challenges that come with real-world physical AI. Your experience and profile: a PhD degree in Computer Vision
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to the position, and intentions and visions for the position Curriculum Vitae (CV) Diplomas (master's degree diploma and PhD diploma) List of publications, with an indication of the attached publications that you
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assist us with the review process. You may upload these documents to the application under CV/Resume. Required Education and Experience Appropriate PhD in a related field Preferred Qualifications PhD in
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
machine learning applied to natural language processing, computer vision, or a related area. Strong publication record in NLP, ML, or related areas -Strong programming skills, including TensorFlow and/or
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collaboration with a leading architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a PhD degree in human-computer interaction, computer
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, large language models, or computer vision is preferred Knowledge, Skills and Abilities: · Teamwork: Ability to collaborate with others and contribute to a team environment · Communication
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vision. Understanding or willingness to learn advanced statistical modeling is a plus Assessment criteria and other qualifications: This is a career development position primarily focused on research
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–based, web-enabled decision-support tool for farmers, using machine learning (random forest) predictive models. Working closely with the PhD student and PIs to ensure rigorous model development