2,792 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in United States
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/Data Science, Statistics or a related field. Experience with statistical analysis, including but not limited to regression analysis and machine learning. Self-motivated, with an ability to drive projects
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: PhD in a scientific field Preferred experience: Strong programming and neuroimaging analysis experience. Familiarity with longitudinal modeling or machine learning techniques is desirable. Why MCW
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mentor undergraduates, graduate students, and/or researchers in the lab. Qualifications Required Qualifications: PhD in biochemistry or a related field. At least one first author publication, including
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, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits , take well-deserved breaks with ample paid time off
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. Required Qualifications: PhD in statistics, economics, computer science, operations research, or related data science fields Strong data science skills, including experience working with large, complex data
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commitment to undergraduate teaching.Today, more than 1,100 faculty members instruct approximately 5,200 undergraduate students and 2,600 graduate students. The University's generous financial aid program
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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., transcriptomics, proteomics, metabolomics) and artificial intelligence/machine learning (AI/ML) applications in biomedical research will be considered a strong advantage. Outstanding UA benefits include health
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Science, Environmental Science, Data Science, or other related fields Strong capabilities and demonstrated experience working with large climate datasets, high performance computing, and machine learning
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, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom machine learning approaches where applicable. Provides feedback and