Sort by
Refine Your Search
-
Listed
-
Employer
- Oak Ridge National Laboratory
- Princeton University
- Northeastern University
- University of Washington
- Argonne
- Brown University
- Nature Careers
- The University of Arizona
- University of California
- University of Kansas
- Brookhaven Lab
- Brookhaven National Laboratory
- Case Western Reserve University
- Fred Hutchinson Cancer Center
- Indiana University
- Lawrence Berkeley National Laboratory
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology (MIT)
- Medical College of Wisconsin
- Michigan Technological University
- Texas A&M AgriLife
- The University of North Carolina at Chapel Hill
- University of California Davis
- University of California Los Angeles
- University of California, Los Angeles
- University of California, Merced
- University of Minnesota
- University of North Carolina at Chapel Hill
- University of Oklahoma
- University of Southern California
- University of Southern California (USC)
- University of Texas at Austin
- University of Utah
- Virginia Tech
- Washington State University
- Washington University in St. Louis
- 26 more »
- « less
-
Field
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
-reviewed publications in relevant fields Strong problem-solving skills and the ability to work in a collaborative environment. Preferred Qualifications, Competencies, and Experience Distributed parallel
-
dynamics (DNS, LES, or RANS) and/or high-performance computing (MPI, GPU, or parallel solvers), as demonstrated by application materials. Evidence of peer-reviewed publications in fluid dynamics, turbulence
-
software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
-
computational fluid dynamics (DNS, LES, or RANS) and/or high-performance computing (MPI, GPU, or parallel solvers), as demonstrated by application materials. Evidence of peer-reviewed publications in fluid
-
multiphase flow in porous media. 80% - Applying numerical and analytical infiltration models to quantify groundwater recharge potential under varying hydrogeologic conditions. In parallel, the researcher will
-
are meaningful in scientific contexts.Preferred:Background in biomedical data, healthcare, or AI for life sciences.Experience with parallel computing.Familiarity with scientific machine learning approaches (e.g
-
smoothly by managing reagents and supplies and performing genomic assays and assisting with long read Nanopore sequencing, functional genomics, RNA IP, RNA probe synthesis and Massively Parallel reporter
-
parallel/multiplexed assays, etc.) is desirable. Ability to interpret and discuss experiments and critically contribute to writing of manuscripts and grant proposals is expected. Well-organized, able
-
deficiency. In parallel, the Deng team is conducting the preclinical studies on developing extracellular vesicles to treat corneal scarring. Both research programs are funded by the National Eye Institute and
-
parallel programming. ● Experience with writing scientific articles. ● Experience with writing scientific machine learning. Overtime Status Exempt: Not eligible for overtime Appointment Type Restricted