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are embedded in established education research-practice partnerships and collaborations with community organizations. These partnerships provide fellows with opportunities to learn to collaborate with
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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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Posted on Sat, 11/09/2024 - 11:35 Important Info Faculty Sponsor (Last, First Name): Gardner, Christopher Other Mentor(s) if Applicable: Clarke, Shoa, MD, PhD; Follis, Shawna, PhD, MS; Henriksen
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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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sequence programming (ex: Pulseq) Python, MatLab, C++, etc PyTorch, TensorFlow ML Ops Required Qualifications: MD, PhD, or equivalent Technical interest & expertise in MRI Required Application Materials: CV
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research expenses. Please direct queries to Professor Lisa Surwillo (surwillo@stanford.edu (link sends e-mail) ). Required Qualifications: Applicants must have received a PhD from an accredited university in
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nano-mechanics, and machine learning as it applies to the field of computational mechanics. Candidates will be given opportunities to develop their teaching experience by designing and teaching a class
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directly engage in course (re)development with faculty, as well as with Stanford’s Center for Teaching and Learning, preparing them for further opportunities as an educator in Bioengineering and related
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survival data using longitudinal features, and (6) machine learning and deep learning for analyzing time-to-event outcomes, or (7) radiomics and medical imaging analysis. Required Qualifications: We seek
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includes contributing to the establishment of a new central laboratory resource, offering these advanced genetic tools to broader research initiatives. Required Qualifications: PhD Required Application