Sort by
Refine Your Search
-
experimental system design, instrumentation, and automation. Knowledge and experience with analytical techniques (e.g., chromatography, spectroscopy, microscopy). Experience with fouling/scaling studies and/or
-
experimental system design, instrumentation, and automation. Knowledge and experience with analytical techniques (e.g., chromatography, spectroscopy, microscopy). Experience with electrochemical techniques (e.g
-
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
-
, as well as other advanced spectroscopic techniques Position Requirements Desired Qualifications Knowledge of and experimental expertise in synthesis and characterization of highly air-sensitive
-
of molecular and/or heterogeneous catalysts Strong understanding of kinetics and thermodynamics as applied to catalysis Knowledge of and experimental expertise in synthesis and characterization of highly air
-
, project presentations, and other regular channels. Position Requirements This level of knowledge is typically achieved through a formal education in chemical engineering, mechanical engineering, or a
-
++ preferred). Knowledge of power system operations, electricity market mechanisms, and energy system modeling. SAnalytical and problem-solving skills, with the ability to work independently and collaboratively
-
, networking, and leadership. Position Requirements Required Knowledge, Skills, and Experience: This level of knowledge is typically achieved through a formal education in economics, operations research, public
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
-
physics knowledge into DL model design and training, these models outperform traditional methods even without labeled training data (https://www.nature.com/articles/s41524-022-00803-w ). Application spaces