Sort by
Refine Your Search
-
, inclusive, and accessible environment where all can thrive. Additional Preferred Qualifications: Working knowledge of power system protection and control. Familiarity with Machine Learning. Familiarity with
-
their environmental impacts including energy use, water use, emissions, and resource depletion. Coursework and some project experience in application of mathematical optimization, statistics, or machine
-
techniques to enable multimodal online monitoring of chemical and radiochemical separations processes Acquire fundamental data relevant to chemical separations in support of related modeling efforts Analyze
-
researchers performing molecular modeling and machine learning activities. The candidate will be expected to perform ion conductivity experiments with thin film polymer electrolytes. The candidate will use
-
(2DIR), and 2D electronic-vibrational (2DEV) spectroscopy are desirable but not necessary Familiarity with experimental setup, including computer interfacing and electronics Job Family Postdoctoral Job
-
must provide proof of U.S. citizenship, which is required to comply with federal regulations and contract. Skill in devising and performing experiments to acquire identified data, using and maintaining
-
The Applied Materials Division (AMD) at Argonne National Laboratory is seeking to hire a Post-doctoral Researcher. The candidate will work within a multidisciplinary team with researchers
-
(typically completed within the last 0-5 years) in material science or related chemistry science with 0 to 1 year of post-graduate experience. Knowledge in the areas of materials science, metallurgical and
-
, which is required to comply with federal regulations and contract. Work conducted under this posting will require the appointee to obtain a security clearance. This level of knowledge is typically
-
in spatial analysis and data visualization Computer programming skills relevant for data manipulation and analysis Experience with creating and using complex data-driven analytical models using R