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environmental trade-offs. Contribute to projects involving capacity expansion, production cost modeling, and equilibrium modeling of power systems. Design and apply mathematical optimization models, including
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techniques in interfacial science; and mathematical techniques and computer programming for data analysis. Considerable skill in working interactively and productively in a multidisciplinary environment Good
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years ) Ph.D. in Engineering, Operations, Computer Science, Mathematics or a related field. Knowledge of optimization, power systems operations and planning, electricity markets, issues surrounding
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-world problems. Position Requirements Recent or soon-to-be completed (typically within the last 0-5 years) PhD in Electrical Engineering, Industrial Engineering, Applied Mathematics, or a closely related
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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within the last 0-5 years) in computational science, mathematics, physics, or a related field with a focus on image processing. Proven experience in algorithm and software development. Expertise in Python
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We are seeking a Postdoctoral Appointee to work in the Mathematics and Computer Science (MCS) Division of the Computing, Environment, and Life Sciences directorate (CELS) of Argonne National
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Leadership Computing Facility (ALCF), the Mathematics and Computer Science Division (MCS), the Computational Science Division (CPS), and the Data Science and Learning Division (DSL). The postdoctoral
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The Physics Division at Argonne National Laboratory invites you to apply for a postdoctoral position beginning fall 2024 at Argonne’s Trace Radioisotope Analysis Center (TRACER). TRACER specializes in detection and analysis of rare noble gas isotopes through Atom Trap Trace Analysis (ATTA), an...
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. Proficiency in frameworks like Pyomo and/or TensorFlow/Pytorch/Keras Solid foundation in mathematics/statistics, with experience in cyber-physical systems modeling. Ability to work both independently and