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of design and innovation on value creation for stakeholders. For more information on D^3/LISH , please visit https://d3.harvard.edu and https://d3.harvard.edu/lish/ . Research Focus: Postdoctoral Fellows
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Position overview Position title: Assistant, Associate or Full Research Scientist Salary range: A reasonable salary range estimate for this position is $120,000-$132,400. The posted UC academic
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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Dept. Course Titles and Course Numbers: enter course title and course number here Estimated Number of Positions: BIOL 112 Cell and Molecular Biology BIOL 201 Cell Biology and Metabolism BIOL 202
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-disciplinary team of researchers, including bioinformaticians, pathologists, oncologists, and computer scientists, and conduct original research on computational pathology. Digital pathology images contain rich
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staff or the public and represent BGSU appropriately Ability to coordinate or lead others in accomplishing work activities Knowledge of basic computer skills Ability to accurately receive and disburse
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policies and procedures; conducts training as necessary. Reviews work orders and blueprints or drawings; inspects site and consults with customers; determines best method to complete a project; estimates
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The Ira A. Fulton Schools of Engineering at Arizona State University (ASU) and the School of Electrical, Computer and Energy Engineering seek applicants for a tenure-track or tenured faculty
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Competencies Knowledge in relevant scientific field. Knowledge of research techniques or methods relevant to the lab (e.g. Microscope imaging, image and data analyses, which includes writing/modifying computer
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 17 days ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional