88 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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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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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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biology, bioinformatics, genetics, AI, machine learning, computer science, or a related field. Demonstrated experience analyzing single-cell and/or spatial genomics datasets is a plus but not necessary
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
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(PhD, MD, or equivalent) conferred by the start date. Proven research and/or professional experience in machine learning and/or natural language processing, with a preference for prior experience working
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. Develop and apply ab initio computations, molecular dynamics simulations, and machine learning models. Collaborate with other researchers within the group and external partners. Present research findings
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Application Materials: To apply, candidates must create an account on Slideroom (link is external) , the application platform used by the Abbasi Program in Islamic Studies, and upload the application materials
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. • Develop computational and theoretical models that bridge neural data and behaviour, leveraging modern machine‑learning toolkits. • Drive multi‑lab collaborations across SCENE; co‑author high‑impact