Sort by
Refine Your Search
-
Listed
-
Employer
- Duke University
- University of Washington
- Oak Ridge National Laboratory
- Argonne
- Cornell University
- National Aeronautics and Space Administration (NASA)
- University of Minnesota
- Princeton University
- Virginia Tech
- Montana State University
- Northeastern University
- Texas A&M University
- University of Colorado
- Washington University in St. Louis
- Brookhaven National Laboratory
- Indiana University
- Rutgers University
- SUNY University at Buffalo
- Sandia National Laboratories
- Stanford University
- TTI
- Texas A&M AgriLife
- The University of Arizona
- The University of Chicago
- University of Arkansas
- University of California Berkeley
- University of Kentucky
- University of Maryland, Baltimore
- University of Miami
- University of North Texas at Dallas
- University of Notre Dame
- University of Oregon
- University of Texas at Arlington
- University of Texas at Dallas
- University of Utah
- University of Vermont Medical Center
- 26 more »
- « less
-
Field
-
simulated and measured results to assess quantities of interest. Interface with world-class exascale computing clusters. Work with a dynamic team of researchers, developers, experimentalists, and model
-
unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
-
reimbursement program (if eligible) Our Employee Benefits TTI employees can choose from several health coverage options offered by The Texas A&M University System for themselves and their families, as
-
, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
-
team, leverage the new cancer outreach and education mobile van to distribute information and provide training about cancer prevention and screening, identify community resources for referral and
-
will be able to access and complete the application through “My Draft Applications” located on your Candidate Home page. Closing Date: 05/31/2026 Type of Position: Professional Staff - Project/Program
-
doctoral degree in Computer Science or Biomedical Informatics or a closely related quantitative field. The successful candidate demonstrates documented expertise in machine learning and distributed
-
adversarial AI and out-of-distribution detection. The successful candidate will have a strong track record in top-tier AI publications, outstanding presentation and leadership skills. Responsibilities
-
computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
-
volumes in frozen cell sections, providing unprecedented maps of the distributions of small molecules within the cell. Determining the spatial distribution of small molecules within cells is crucial