Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
implementation for core capabilities such as distributed training and inference, workflow orchestration, GPU/accelerator utilization, model registries and artifact management, vector search and retrieval-augmented
-
analyzing machine learning experiments; supporting scaled experiments involving neural networks, large language models, and GPU clusters; improving reproducibility, reliability, and usability of research
-
research workflows. Maintain and evolve shared AI infrastructure including GPU-enabled workstations, AI development environments, and HPC resources used by students and researchers. Develop and maintain
-
automation tools (e.g., Ansible, PowerShell) and scripting languages. Strong understanding of CPU and GPU environments, network protocols, storage solutions, and cloud technologies. Knowledge of security best
-
infrastructure--- including GPU-enable units and HPC server Manuscript and grant preparations --- for all research pertaining to mental health NLP and behavioral work. And to support grant administration, co
-
, and administers CPU/GPU HPC clusters, including management and compute nodes, storage infrastructure, interconnects such as InfiniBand, and physical infrastructure in the datacenter and related systems
-
CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 3 months ago
HPC clusters and GPU resources to support computational biology, genomics, and machine learning workloads; Manage virtualization and containerization platforms (VMware, KVM, Docker, Apptainer
-
technical support for research projects. Familiarity with shared memory and distributed memory parallelism (e.g., OpenMP, MPI, OpenACC), accelerators (e.g., GPUs), and large-scale file systems. Proven
-
archiving solutions to collaboration and analytics tools. ARC also delivers Baskerville; a leading GPU accelerated National Compute Resource (NCR) and supports researchers using specialist regional and
-
(including numerical optimisation, variational methods and MCMC sampling) C3 State-of-the-art deep learning (including transformers, graph neural networks and normalising flows) C4 Use of GPU programming and