Sort by
Refine Your Search
-
Employer
- Argonne
- University of North Carolina at Chapel Hill
- National Aeronautics and Space Administration (NASA)
- Princeton University
- Oak Ridge National Laboratory
- Baylor College of Medicine
- Cornell University
- Los Alamos National Laboratory
- New York University
- Purdue University
- Texas A&M University
- The Ohio State University
- The University of Iowa
- University of Arkansas
- University of Cincinnati
- University of Kansas
- University of Minnesota
- University of Minnesota Twin Cities
- University of South Carolina
- University of Texas at Arlington
- University of Virginia
- University of Washington
- Zintellect
- 13 more »
- « less
-
Field
-
-of-the-art sparse algorithm in matrices, tensor and networks for large-scale numerical, scientific and AI models and disseminating findings through publications and presentations in top-tier peer-reviewed
-
to numerous preclinical research projects focused on the development of novel molecular magnetic resonance imaging (MRI)-based techniques for early detection, disease phenotyping and monitoring treatment
-
after memory is established. ? Numerical analysis of complex data sets to evaluate the neural representation of information in the physiological recordings above. ? Collaboration
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously
-
machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
-
protocols of interest are 61, 62 (bioburden testing), 71 (sterility testing), and 85 (bacterial endotoxin testing). The researchers will be asked to optimize lab-specific protocols and run the protocols at a
-
to optimize lab-specific protocols and run the protocols at a larger scale for prototype testing. The candidate is also expected to perform the biological testing detailed in the aforementioned protocols in
-
access to state-of-the-art numerical models and high-performance computing systems at Princeton and in NOAA, working alongside GFDL model developers and software engineers to advance quality assurance and
-
, optimization and the modeling of critical infrastructure (gas pipelines and electrical grids). These application areas are tied together by research in novel algorithms, numerical methods and machine learning
-
, numerical optimization, numerical partial differential equations, and parallel computing. The Researcher will join a project developing parallel high-order meshing algorithms from medical images and parallel