Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
programming LAMP stack design and implementation experience Knowledge of GPU and FPGA cluster management Experience with federal research compliance and security requirements Background in AI/ML computing
-
software aspects of large-scale AI systems. Areas of interest may include, but are not limited to: • Advanced accelerator chip technologies, such as GPUs or other specialized chips for large-scale AI
-
/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g., concept bottlenecks, prototype
-
, resource requests, and environment management. Desired Requirements: 1. Probabilistic modeling: scVI/scANVI/totalVI for RNA and RNA+protein integration. 2. GPU experience: PyTorch/CUDA for segmentation/model
-
Appointment Term: 1-2 years Appointment Start Date: January 2026 Group or Departmental Website: https://greiciuslab.stanford.edu/ (link is external) How to Submit Application Materials: Please email application
-
the College of Engineering. UNLV GPU Cluster (named RebelX) is also available for A.I. research and education. Detailed information about the CEEC Department can be found at: http://www.unlv.edu/ceec MINIMUM
-
of existing bioinformatic workflows and development of new pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation
-
3T Siemens MR scanners, OPM-MEG, EEG, eye tracking, and TMS laboratories. They will also have access to Princeton's world-class computational infrastructure, including GPU systems capable of running
-
the EU’s ambitious AI Factories initiative. Learn more: https://mimer-ai.eu/about-mimer/ , https://www.naiss.se , https://eurohpc-ju.europa.eu/ai-factories_en The position As AI Training Program Officer, you
-
projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities