Sort by
Refine Your Search
-
with computational scientists to apply machine learning techniques to optimize for performance and power consumption. Experience in device modeling and circuit-level analysis is required. Candidates with
-
provided to participate and collaborate with cutting edge programs in machine learning and synchrotron science. The applicant is expected to hold a Ph.D. with background expertise in chemistry, geochemistry
-
application. In your cover letter, please describe your previous experience with materials microscopy simulation/analysis; describe your experience with machine learning/computer vision; describe your software
-
teamwork. Preferred skills and qualifications: Experience in modeling electrochemical processes. Experience modeling ionic solutions. Experience developing machine learning force fields. Experience with high
-
with opportunities to participate and collaborate with cutting edge programs in machine learning and synchrotron studies. The applicant is expected to hold a Ph.D. with knowledge and expertise in
-
combustors with gaseous fuels for stationary power generation applications. Focus on de-carbonization of this sector via the use of Carbon-less and low-Carbon fuels. Develop machine learning based reduced
-
, respect, integrity, and teamwork. Desired skills and qualifications: Expertise in computer science, supercomputing, machine learning, AI. Application materials required: Upon submitting your application
-
expertise in materials synthesis to develop novel catalysts guided by machine learning algorithms Catalyst Performance Evaluation: Utilize aqueous electrochemical combinatorial techniques to evaluate
-
Argonne National Laboratory in Lemont, IL is seeking a Postdoctoral Appointee in the Materials Science Division in Computational Materials Chemistry and Machine Learning. The postdoctoral researcher