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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 3 hours ago
to minimize algorithmic bias. Develop expertise in evaluating AI devices that can adapt and learn post-deployment, including understanding evolving algorithms and creating methodologies to assess algorithm
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algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing extreme-scale scientific data management
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inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security
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at the University of Maryland School of Medicine (UMSOM) is seeking a highly motivated postdoctoral fellow for a full-time, three-year appointment. The fellow will contribute to an NIH-funded project developing a non
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theory, and statistics. Algorithm Development: Ability to design and optimize AI/ML models for neuroscience. Scientific Writing & Communication: Ability to publish research and present findings. Basic
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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on the design, development, and realization of future communications technologies. You will be part of the team and contribute to ongoing developments in theory, algorithms and translation to practice in
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the cellular and molecular pathways disrupted in brain disorders such as schizophrenia and autism, by utilizing recent advances in genetics and genomics. We are developing and applying tools to understand how
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software development, cloud computing, data engineering, algorithm design, or scalable computational workflows is a strong plus. Excellent written and verbal communication skills, with the ability to work
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for tumor behavior and clinical outcomes Development and implementation of artificial intelligence and machine learning algorithms for biologically and clinically motivated questions in pediatric oncology