Sort by
Refine Your Search
-
Listed
-
Employer
- Argonne
- University of North Carolina at Chapel Hill
- Duke University
- Oak Ridge National Laboratory
- Stony Brook University
- University of South Carolina
- Yale University
- Brookhaven Lab
- Embry-Riddle Aeronautical University
- Harvard University
- New York University
- Northeastern University
- Stanford University
- The Ohio State University
- The University of Arizona
- University of Colorado
- University of Minnesota
- University of North Texas at Dallas
- University of Texas at Arlington
- 9 more »
- « less
-
Field
-
environments, cloud computing, or GPU-accelerated machine learning Background in Monte Carlo Tree Search (MCTS) or reinforcement learning for sequence generation Familiarity with biological sequence alignment
-
mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
-
grid analytics, and scientific imaging. The successful candidate will design and implement sparse algorithms for large-scale scientific and numerical computations. This role offers an exceptional
-
well as market and organization considerations. Education: Ph.D. in machine learning, computer science, engineering, science or related technical discipline. Experience: Expertise in developing and training AI
-
Sign In Create Profile Postdoctoral Research Associate-Computer and Information Research Tucson, AZ, United States | req23101 Apply Now Share Save Job Posted on: 6/11/2025 Back to Search
-
computational infrastructure such as A100 and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research
-
program of independent research with the primary goal of improving the algorithm codes used to characterize the Earth’s gravitational field. Duties will include specialized scientific/numerical analysis
-
Computer Science, Applied Mathematics, Physics, Computational Biology, Neuroscience with Computational or Theoretical focus, or a closely related field. Preferred Qualifications: Familiar with Information Theory
-
Computer Science, Applied Mathematics, Physics, Computational Biology, Neuroscience with Computational or Theoretical focus, or a closely related field. Preferred Qualifications: Familiar with Information Theory
-
, the Associate will assist with research and development projects in the areas of robotics, machine learning, control systems, and/or computer vision. They will be responsible for: designing and analyzing