54 finite-element-analysis Postdoctoral positions at Stanford University in Postdoctoral
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experimental platform for spatial multi-omic analysis of biological tissues. Our lab builds biological measurement infrastructure—engineering systems that standardize how information is extracted from complex
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national network of collaborators with similar research programs. Required Qualifications: Highly motivated postdoctoral researcher with: Experience in relational databases, big data curation and analysis
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. The project will also intersect with analysis of extracellular vesicle and with other therapeutically relevant agents. Findings will be validated in human tissue samples to ensure biological relevance. By
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data analysis, machine learning and computer vision. The position will be based at Stanford University. The fellowship has two primary goals: to advance our research program on the health impacts
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primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science, and
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· database extraction · data cleaning · analysis · algorithm development and implementation · data visualization [CI1] The postdoctoral
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at the time of application. Experience in molecular and cell biology techniques, preferably in single cell sorting (FACS), scRNA-seq, spatial transcriptomics and very strong multi-omic data analysis skills
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well as the analysis of retinal ganglion cell survival by immunohistochemistry. Record required of first author paper published or accepted for publication demonstrating relevant skills and expertise. Required
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analysis and dissemination. We are looking for candidates pursuing a clinical science-oriented career who excel at both the provision of cognitive behavioral psychotherapy and at research. The fellow will be
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neuroimaging data analysis. Extensive experience with functional magnetic resonance imaging, including resting fMRI and task fMRI. Proficiency in handling NIfTI-format neuroimaging data and performing volume