77 parallel-and-distributed-computing-phd-"Multiple" Postdoctoral positions at Stanford University in United States
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and pain research Required Qualifications: PhD (or equivalent) in epidemiology, health data science, biomedical informatics, biostatistics, public health, or a related field. Demonstrated experience
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test the hypothesis that the targeting of multiple mutations will enhance survival in glioblastoma. We propose to identify an amino acid substitution that can enhance proteasome processing followed by
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federally funded, interdisciplinary research program focused on improving healthcare delivery by studying and intervening in the human systems that support it. Based in the HEAL and Kim Labs at Stanford
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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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common equipment, and also have the benefit of access to research facilities at Stanford University including core computing, microscopy, library, biostores, and analytical facilities. The Spin lab has
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Affairs. The FY25 minimum is $76,383. Our postdoctoral research fellowship program is dedicated to preparing scholars for an academic career in the domains of pediatric perioperative, pain, sleep, and/or
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collaborative culture. The Division of Pain Medicine is at the forefront of innovation in pain research, education, and patient care. Our postdoctoral program has successfully transitioned fellows
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on manuscripts, presentations, and research proposals Required Qualifications: PhD in psychology, neuroscience, biostatistics, computer science, or a related field. Strong interpersonal and technical skills
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their knowledge and skillset in mitochondrial biology would best fit this position. Required Qualifications: PhD in cell biology, molecular biology, stem cell biology, developmental biology, immunology, or cancer
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. Required Qualifications: Doctoral degree (PhD) conferred by start date Demonstrated experience with analysis of large health databases Training and experience in machine learning and deep learning methods