35 computer-vision-and-machine-learning Postdoctoral positions at University of Washington
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activities and psychological stress. Duties/Responsibilities The researchers will contribute specifically through: Gathering data, developing and implementing machine learning models, and interpreting findings
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repository and computer servers. Run existing PET/MR brain image processing pipelines on the computer servers, produce the results, and communicate with the group members. Write computer codes for the above
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. Experience with high-throughput molecular biology assays. Experience with complex functional experiments. Background in machine learning, AI, or data integration for genomic datasets. Familiarity with gene
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encouraged to apply: Biologists with mathematical, computational, or programming experience. Prior experience with mouse models, motivated to learn new techniques including advanced optical imaging
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interpreting wet-lab synthesis data are encouraged to apply and will have opportunities to explore machine learning-guided approaches in chemistry. In addition to excellent research skills, we are seeking
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for policy decisions based on predictions from statistical and machine learning models Postdoctoral scholars are represented by UAW 4121 and are subject to the collective bargaining agreement, unless
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biologist to join our interdisciplinary team of research biologists, medical doctors, engineers, mathematicians, and computer scientists studying cancer and other human diseases. We are at the forefront
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techniques, applying computational neuroimaging, machine learning and fluid dynamics methodologies to interpret these complex datasets. They will work closely with a dynamic team comprised of neurosurgeons
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in adaptive immune systems (e.g., co-evolution of bacteria and phages, as well as T and B cells with pathogens). • Physics-informed machine learning of biophysical systems (e.g., developing optimal
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Position Summary The DRIVES Project is seeking a Postdoctoral Research Associate with interests in Alzheimer’s disease (AD), biomarkers, driving, machine learning, and/or geographic information