132 fully-funded-phd-program-computer-science-eth Postdoctoral positions at Stanford University
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Fetal Medicine Center for Discovery, Innovation and Impact. Required Qualifications: PhD in developmental biology, genetics, cell and molecular biology, or a related field. Strong background and expertise
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Stanford food systems working group (link is external) . The postdoc will be responsible for writing proposals seeking funding to expand and sustain these research ideas. This fellowship is confirmed for one
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cultures to uncover the biological mechanisms underlying resilience in APOE4 carriers. EDUCATION AND EXPERIENCE: ● PhD in neuroscience, life sciences Required Qualifications: KNOWLEDGE, SKILLS, AND ABILITIES
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a PhD in the field of molecular and cellular cancer biology, relevant publications, curiosity for science and innovative thinking, and high fluency in English. Experience with mammalian cell culture
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: Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including bioengineering, computer science, statistics, or mathematics. Strong
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Engineering Postdoc Appointment Term: This position is for a one-year fixed term, with possible extension for a second year subject to funding availability. Appointment Start Date: September 1, 2024 Group
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Posted on Mon, 11/11/2024 - 12:40 Important Info Faculty Sponsor (Last, First Name): Qiu, Xiaojie Stanford Departments and Centers: Genetics Computer Science Postdoc Appointment Term: Initial 2
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University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $76,383. **This position is fully funded for at least two years, and is NOT
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strong background in one or more of the following areas: computational biology, genomics, biochemistry, or neuroscience. A strong publication record demonstrating expertise in the relevant field. Team
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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