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for Postdoctoral Research Fellow in AI and Computational Neuroscience. Our lab focuses on understanding age-related cognitive decline and neurodegenerative diseases, with a special emphasis on Alzheimer's disease
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. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master various computer
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availability, and internal equity. Pay Range: $80,000 to $90,000 We invite applications for a Postdoctoral Research Associate to join a collaborative experiment–theory program investigating electron–ion
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-polysaccharide assemblies to naturally occurring and engineered polymers and industrially relevant catalytic materials. The Opportunity The successful candidate will lead an interdisciplinary research program in
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Laboratory at Stanford University is seeking a highly motivated postdoctoral scholar to join our interdisciplinary team working at the intersection of liquid biopsy, cancer genomics, and computational biology
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on physiologic waveform analysis, biomedical signal processing, and computational modeling of continuous clinical monitoring data. The successful candidate will work on projects involving the analysis
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(but not limited to) the qualifications of the selected candidate, budget availability, and internal equity. Pay Range: $80,000 - $85,000 Postdoctoral Research Fellow - Computational Neuroscience and
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to candidates with strong training in metabolism or adipose biology, neuroscience, genomics, physiology, molecular biology, computational biology, or a related field, who are eager to build an interdisciplinary
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disorders, particularly autism, using novel computational approaches, neuroimaging-derived brain circuit fingerprints and transcriptomic signatures. A second project focuses on developing multimodal
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-disciplinary approach spanning human genetics, computational biology, genomics, genetics, pharmacology, and biochemistry. By integrating human population-scale data with experimentally tractable model systems