Sort by
Refine Your Search
-
. Position Requirements A formal education in materials science and engineering, nuclear engineering, or a related field at the PhD level with zero to five years of employment experience. Knowledge of metallic
-
research in the field of metallization and decarbonized metal production, using methods such as aqueous electrolysis, non-aqueous electrolysis, and molten salt electrolysis. The candidate will conduct
-
micro-CT, ultrasonic methods, and x-ray-based characterization Contribute to hardware and software development for experimental platforms Process, interpret, and manage large datasets Present research
-
In the Computational Materials Group, we focus on the development and use of computational and theoretical methods to understand and predict the behavior of solids, liquids, and nanostructures from
-
partners. Responsibilities will include designing and conducting experiments, developing slurry-based processing methods, building and operating materials recovery systems, analyzing material transport and
-
activities. This is an exciting opportunity to work within a collaborative group with deep expertise in silicon detectors, Trigger/DAQ (TDAQ) systems, software, and computing. The Argonne ATLAS group plays a
-
, presentations, software, datasets, and internal reports Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in chemistry, chemical engineering, materials science, polymer science
-
. Your responsibilities will encompass pioneering novel synthesis doping techniques for "quantum grade" diamond, optimizing surface termination methods, and developing deterministic synthesis of pertinent
-
methods for analysis, tuning, and control of particle beams and accelerator systems, with applications to the APS injector and the newly-upgraded APS storage ring. This work will explore applications
-
together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for multimodal federated learning