Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
provide a performance or efficiency advantage, and determine scenarios where conventional AI accelerators (such as embedded GPUs or FPGA-based accelerators) remain more appropriate due to data
-
signal processing (GPU based) Proficiency in data analysis using Python, Matlab, or similar Self-motivating, independent-minded scientific researcher, effective collaborator Excellent written and oral
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
workloads with dedicated GPU and large-memory partitions. The Research Triangle area is a dynamic collaborative environment with UNC-Chapel Hill, Duke University, and North Carolina State University all
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
, including the 17,000-core Longleaf cluster optimized for I/O intensive workloads with dedicated GPU and large-memory partitions. The Research Triangle area is a dynamic collaborative environment with UNC
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 23 days ago
workloads with dedicated GPU and large-memory partitions. The Research Triangle area is a dynamic collaborative environment with UNC-Chapel Hill, Duke University, and North Carolina State University all
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 23 days ago
, including the 17,000-core Longleaf cluster optimized for I/O intensive workloads with dedicated GPU and large-memory partitions. The Research Triangle area is a dynamic collaborative environment with UNC
-
. Preferred Qualifications Experience with: C/C++, Python, MATLAB, ROS 1 and 2, OpenCV, Unity, GPU programming, linear and nonlinear control theory, supervised, unsupervised and reinforcement learning, Torch
-
experimental data. Experience in GPU programming. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is
-
research in ML for Health, including HIPAA-compliant compute infrastructure with high memory GPUs and access to Stanford Healthcare data, which includes EHRs for over 5M patients and 100M clinical notes
-
. Experience in parallel programming (MPI, GPU, etc.). Proficiency in biostatistical methods. Ability to work independently and in group settings. Ability to learn quickly and apply new analytic techniques. Job