41 algorithm-development-"Multiple"-"Prof"-"Prof"-"SUNY" Postdoctoral positions in United States
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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, focusing on intelligent sensor tasking and the automated identification and characterization of space objects in Earth orbits and cislunar environment using optical data. Contribute to the development
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algorithms, and machine learning to solve complex aerospace engineering challenges. Developed and implemented AI-driven solutions for autonomous lunar and asteroid landings, as well as cislunar operations
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| Mechanical and Aerospace Engineering Perform basic research in computational fluid dynamics, including problem setup, simulation and advanced post-processing for multiple projects. Emphasis to be placed
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of pediatric cancer patients and discover the impact of therapeutic exposure on development of secondary neoplasms in adult survivors. Position Responsibilities: Evaluate bulk-tumor deconvolution algorithms
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develop signal processing algorithms to characterize structural health in microreactors and other advanced nuclear reactor technologies. Metrics for success will include scientific output, disseminating
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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magnetometry measurement and analysis of the Nab spectrometer magnetic fields, help develop the BL3 DAQ and algorithms, carry out Monte Carlo simulations for Nab and BL3, and help undergraduates at EKU finish
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postdoctoral scholars, working on the development of a core, scalable methodology. This methodology leverages existing spatial data on landscapes, fire behavior, and fuel treatments to evaluate real-world