Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
Assistant Research Professor (Research Track), Radiology and Imaging Sciences Posting Number req24876 Department Radiology & Imaging Sci Dept Department Website Link https://radiology.medicine.arizona.edu
-
managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
-
that serves researchers and educators at the University of Utah and beyond. Responsibilities Kubernetes for AI/ML: Design and deploy highly available Kubernetes clusters, optimized for GPU utilization and AI/ML
-
that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance
-
dynamics simulations using parallel computers or GPU computers. We correlate the obtained results with experimental data on polymeric functional materials to elucidate their dynamic behavior and clarify
-
) Experience in maintaining high-quality code on Github Experience in running and managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team
-
Microscopy Center. The project further benefits from excellent dedicated CPU and GPU computing infrastructure to support large-scale numerical modelling and data analysis. This is a full-time, two-year
-
dedicated CPU and GPU computing infrastructure to support large-scale numerical modelling and data analysis. You will receive extensive training in these techniques as part of your PhD project and will work
-
at the Technical University of Munich (TUM) invites applications for one PhD position. The student will work on developing scalable distributed preconditioners in Ginkgo (https://github.com/ginkgo-project/ginkgo
-
to push the boundaries of what’s possible. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. We don’t