88 image-processing-and-machine-learning "UCL" Postdoctoral positions at Rutgers University
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, walking, reaching, kneeling, talking and hearing. Ability to lift up to 25 lbs. WORK ENVIRONMENT: Scientific research laboratory. Computer data input. Universal safety precautions are mandatory. Lab
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publications. Expertise in human stem cell culture, immunocytochemistry, confocal or advanced imaging, and related molecular biology techniques is highly desirable. In addition to experimental contributions
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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are invited for motivated scholars with a strong background in synaptic physiology and live imaging to join the laboratory of Pingyue Pan at the Department of Neuroscience and Cell Biology at Rutgers University
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skills; must be proficient in spoken and written English. The ability to work both independently and collaboratively is essential. Must be computer literate with proficiency and working knowledge
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communication skills. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel, Access, and PowerPoint. Preferred Qualifications Equipment
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SAS and/or R programming skills. Effective oral communication skills. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel
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. CCA Postdoctoral Associates receive a salary of $50,000, health benefits, a private office, and administrative support. Fellows normally teach 1 undergraduate course during their fellowship year. Since
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. CCA Postdoctoral Associates receive a salary of $50,000, health benefits, a private office, and administrative support. Fellows normally teach 1 undergraduate course during their fellowship year. Since
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Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb and joint morphology across