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and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
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algorithms and complexity theory, including in both well-established settings (e.g., sequential computation on a single machine and distributed/parallel computation on multiple machines) as well as emerging
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: Algorithm Design and Analysis CSE 107: Introduction to Digital Image Processing CSE 108: Full Stack Web Development CSE 111: Database Systems CSE 120: Software Engineering CSE 160: Computer Networks CSE 168
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: Algorithm Design and Analysis CSE 107: Introduction to Digital Image Processing CSE 108: Full Stack Web Development CSE 111: Database Systems CSE 120: Software Engineering CSE 160: Computer Networks CSE 168
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Structures CSE 031: Computer Organization and Assembly Language CSE 100: Algorithm Design and Analysis CSE 107: Introduction to Digital Image Processing CSE 108: Full Stack Web Development CSE 111: Database
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that include symbolic AI and formal methods (logic, theorem provers, type systems, categories, etc.), concurrent programming languages (choreographic programming, session types, etc.), distributed computing
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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employers. About the Global Campuses The university campuses operate as a distributed global network. For example, new programs may be developed and established at one of the network campuses, enhanced
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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the project to have well-distributed data both in space and time. This will ultimately lead to higher quality (more spatially and temporally accurate, complete, precise) 3D models. However due to the complexity