Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
(AWS, Azure/GCP) Experience in open source software development. Knowledge of GPU-based computing, including multi-gpu/multi-node parallelization techniques will be valued. Fluency in spoken and written
-
leading-edge scientific challenges and needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as
-
challenges and needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as well as access to the National
-
IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 2 months ago
, · collaboration with application developers and domain experts on highly scalable parallel applications with focus on: - development and implementation of parallel aplications, - GPU acceleration of applications
-
educational mission at OU. OU is continuing to expand its internationally known radar program in collaboration with its School of ECE. The Advanced Radar Research Center (ARRC, http://arrc.ou.edu ), which
-
(PyTorch, TensorFlow). Experience with dataset curation, annotation workflows, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data processing
-
the U.S. (https://www.rc.ufl.edu/about/hipergator/ ), and the AI NVIDIA GPU SuperPOD (https://news.ufl.edu/2020/07/nvidia-partnership/ ) supporting UF’s campus-wide AI initiative (https://ai.ufl.edu
-
://www.rc.ufl.edu/about/hipergator/ ), and the AI NVIDIA GPU SuperPOD (https://news.ufl.edu/2020/07/nvidia-partnership/ ) supporting UF’s campus-wide AI initiative (https://ai.ufl.edu ). These resources are available
-
the computer science research conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings
-
and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA