2,728 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in United States
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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, Abilities and Skills: -Strong analytical, organizational, computer and communication skills. -Ability to multi task and work cooperatively with others. Preferred Knowledge, Abilities and Skills: -Strong
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computer programming in R and Python *Formal training or experience applying quantitative and spatial methods to human-environment questions *Excellent academic writing and communication skills in English
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Mohadeseh Taheri-Mousavi’s group. The postdoc will develop and conduct advanced machine learning techniques combined with computational research to study the mechanical behavior of welds. Responsibilities
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any of the following areas: Neuroscience, Pharmacology, Physiology, or Biochemistry. Computer Skills Microsoft Office Suites Supervisory Responsibilities No Required operation of university owned
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of mouse behavioral paradigms is required. Extensive computer programming experience in python or Matlab and the ability to deal with large, complex datasets are required. Experience with preparation and
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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/vision, dental, flexible spending accounts, life, disability, retirement, family life support, employee assistance program, onsite health clinic, income-based child care subsidy, tuition reimbursement
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with various methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning and applying these techniques to problems in computational creativity