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Field
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Argonne National Laboratory has a long-standing tradition of attracting top early career talent through the Named Fellowship Program. These prestigious fellowships are awarded internationally each
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machine learning methods • Proficiency in transport planning software such as EMME or PTV Visum. • Programming expertise in at least one language, e.g., Python, Java, or C++. • Strong written and
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optimization algorithms is highly desirable Excellent programming skills in Python, C++, Java, Julia, or other relevant languages Knowledge of maritime decarbonization and alternative fuels is a plus A good
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++, Java, Julia, or other competent languages. A good record of publications in reputable peer-reviewed journals in maritime transport, logistics management, machine learning, deep learning, and optimization
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community that includes departments across the UChicago campus as well as Argonne National Laboratory , Fermi National Accelerator Laboratory , the Marine Biological Laboratory , and Toyota Technological
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for a postdoctoral research fellow. The position is part of a U.S. Department of Energy Genesis Mission project that brings together the University of Michigan, Los Alamos National Laboratory, and Argonne
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experience may include tools and languages such as Python, MATLAB, R, Java, and optimisation packages or solvers such as Gurobi, CPLEX, Pyomo or equivalent platforms. Experience with modelling or simulation
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such as Python, MATLAB, R, Java, and optimisation packages or solvers such as Gurobi, CPLEX, Pyomo or equivalent platforms. Experience with modelling or simulation environments such as AnyLogic, MATSim
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university; Excellent programming skills, such as Python, C++, Java, Julia, or other competent languages; A good record of publications in reputable peer-reviewed journals or conferences in maritime transport
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Models. Experience with deep learning frameworks such as PyTorch or TensorFlow. Proficiency in programming languages including C/C++, Python, Java, and Go. Familiarity with Digital Content Forensics