118 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
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machines that both learn from humans and help humans learn. The postdoctoral fellow will lead a project using AI technologies to support active learning in young children, by empowering them to create
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blockade (Phillips, Matusiak, et al, Nature Communications, 2021). We do research at the forefront of spatial biology and offer training in immunology, human histology, statistics, computer vision, grant
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it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web
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focus on machine learning in the Stanford Center Cancer Cell Therapy at Stanford University School of Medicine. We seek a highly creative and motivated scientist to perform cutting-edge computational
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disciplines such as physics, statistics or math. Have acquired machine learning, generative AI and computer science. Welcome either wet or dry background or both. Be highly creative, rigorous, collegial and a
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, or MATLAB) are required. Knowledge in one or more of the following areas is desirable: biomedical imaging, biomedical optics, computer vision, bioinformatics, single-cell profiling technologies, spatial omics
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transcriptomics analysis • Interest in cancer biology and immunology principles • Excellent written and verbal communication skills Preferred Qualifications: • Experience with machine learning approaches
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presentations). Evidence of their contributions to their current research communities. Track record of mentoring more junior scholars. Required Qualifications: PhD in computer science, electrical engineering
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include, but are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance
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Program at the Stanford Cancer Institute. She has an academic interest in Precision Medicine and her lab applies cutting-edge sequencing and imaging technologies to better understand skin cancer and rare