Sort by
Refine Your Search
-
communication skills, and ability to interact with people at all levels both within and outside the laboratory. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork
-
advanced computing, optimization, and data analytics technologies. The postdoctoral researcher will work with a team of researchers on solving challenging problems using optimization, stochastic models
-
We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced
-
to the development of new research directions aligned with program goals. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Chemical Engineering, Materials
-
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
-
to microelectronics. The candidate will be part of a highly interdisciplinary project involving X-ray scientists, physicists, materials scientists, and computational scientists to solve challenging problems in
-
detectors while also having flexibility to pursue your own research interests. Research Focus Participate in a detector R&D program aimed at developing superconducting nanowire sensors to enable
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
vulnerabilities. The Postdoctoral Appointee will be responsible for the conceptual framework, design, and implementation of these models, ensuring scalability on the DOE’s leadership computing facilities. Position
-
of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
-
turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and