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Field
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experience in the following topics: object-oriented programming, data structures, discrete structures, computer organization, machine learning, deep learning, algorithms, science of programming, computer
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. The selected candidate will contribute to one or more research projects within the Systems Genetics group led by Professor Enrico Petretto (Principal Investigator). These project(s) will be conducted
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management reporting needs. Partner with programmers to design and institutionalize accurate forecasting and financial planning tools; this includes modeling new algorithms, creating functional specifications
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, algorithms, product changes, competitive landscape) and translate them into practical strategy and guidance for editors and producers. Work with editors and producers across WAMU News, 1A, and other
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of concepts, algorithms, predictive models, and custom analysis. Design and build new data set processes for modeling, data mining, and production purposes. Duties and Responsibilities : Research discrepancies
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: Reinforcement Learning (RL) in Complex Environments: Offline and online RL with high-dimensional, unstructured variables, partially observed states, and hidden actions; New methodological and algorithmic
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at the leading edge of scientific discovery and will therefore require methodological work, algorithm development, and technical developments. The ideal candidate will be proficient in R and/or Python, have strong
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management reporting needs. Partner with programmers to design and institutionalize accurate forecasting and financial planning tools; this includes modeling new algorithms, creating functional specifications
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transportation contexts, and machine learning classifiers Heuristics & Solvers: Develop and refine custom heuristics and metaheuristics (e.g., Tabu Search, Genetic Algorithms) to find high-quality solutions
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science algorithms (e.g. regression, classification and clustering techniques, decision trees and random forests, machine learning techniques like supervised, unsupervised and reinforcement learning