94 software-engineering-model-driven-engineering-phd-position Postdoctoral positions at Stanford University
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Appointment Start Date: ASAP How to Submit Application Materials: Please email applications to Dr. Max Diehn ([email protected](link sends e-mail) ). Does this position pay above the required minimum
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and DNA bar coding technology strongly preferred. Required Qualifications: PhD in immunology, molecular biology, or a related field. Required Application Materials: Please send a letter describing your
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this position pay above the required minimum?: No. The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral
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Materials: Email Jonathan Pollack at [email protected](link sends e-mail) Does this position pay above the required minimum?: No. The expected base pay for this position is the Stanford University
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prognostic markers for psychosis in youth at clinical high risk, using state-of-the-art AI models and multimodal neuroimaging, clinical, and cognitive data. The position will emphasize advanced computational
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for highly motivated postdoctoral candidates with a PhD in bioengineering deep knowledge in computational biology and machine learning. Candidates with a molecular biology or engineering degrees with
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to Submit Application Materials: To submit your application, please complete the following form: https://tinyurl.com/4e4xtdux(link is external) Does this position pay above the required minimum
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) . Does this position pay above the required minimum?: Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected candidate will be determined based
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(1-2) Applicants with expertise in one or more of the following areas are encouraged to apply: * Foundation Models * Agentic AI * Reinforcement Learning * Medical Image Analysis Position 2: Intelligent
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care for patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial