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deposition (ALD). The project involves performing quantum mechanical calculations (e.g., first principles density functional theory (DFT)) to identify the structures and to understand the complex mechanisms
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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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: Experience with generative models (transformers, diffusion models, VAEs, GANs) applied to biological sequences Familiarity with the theory behind modern molecular biology techniques Knowledge of HPC
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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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apply analytical models and datasets in collaboration with DOE national laboratories and federal partners. Prepare detailed reports and briefings on methodologies, analyses, and findings. Collaborate with
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(microelectromechanical systems) devices for X-ray optics at synchrotron radiation sources. Some background of the project is given in the publications listed below. The idea is to make highly nonlinear MEMS-based
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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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or any other characteristic protected by law. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position
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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