48 phd-mathematical-optimization Postdoctoral positions at Duke University in United States
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Duke University, Department of Mathematics Position ID: Duke -POSTDOCASSOC [#26970] Position Title: Position Type: Postdoctoral Position Location: Durham, North Carolina 27708
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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the nation. The school consists of four departments with 130 tenure-track faculty members, 1250 undergraduate students, 1400 master’s students, and 600 PhD students. Housed within a university renowned for its
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quantitative methods and excited about discovering physical principles of biological organization. Minimum Requirements: PhD in a scientific disciplines, ideally Biology, Bioengineering, Physics or Math
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of the mass spectrometry-based proteomic data generated in our chemoproteomics experiments. The postdoctoral associate will be responsible for developing and optimizing custom data analysis and visualization
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motivated individuals to join our dynamic team as Postdoctoral Associates. Scope of Work: • Lead the design and synthesis of novel chemical entities. • Optimize reaction conditions and processes
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tenure-track faculty members, 1250 undergraduate students, 1400 master’s students, and 600 PhD students. Housed within a university renowned for its programs in the liberal arts, medicine, business and law
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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restoration of function. The successful applicant will combine computational modeling, engineering optimization, and in vivo experiments to advance understanding and application of electrical block of neural