Sort by
Refine Your Search
-
Employer
- Harvard University
- Simons Foundation;
- Center for Devices and Radiological Health (CDRH)
- Dana-Farber Cancer Institute (DFCI)
- Florida Atlantic University
- Georgia Southern University
- Oden Institute for Computational Engineering and Sciences
- SUNY University at Buffalo
- University of California
- University of Idaho
- University of Maryland, Baltimore
- University of Texas Rio Grande Valley
- Zintellect
- 3 more »
- « less
-
Field
-
faculty onboarding, consultation, documentation, and training activities that help research teams become productive users of HPC and GPU resources Assist faculty in identifying and pursuing external
-
resources, including 2 HPC clusters with a combined 180,000 cores, 600 GPUs, and 60PB of raw storage. FRFs may be eligible for subsidized housing within walking distance of the CCA. These positions will be
-
research budget and have access to the Flatiron Institute’s powerful scientific computing resources, including 2 HPC clusters with a combined 180,000 cores, 600 GPUs and 60PB of raw storage. FSRFs may be
-
· Experience with multi-GPU or distributed training is a plus · Ability to work independently as well as part of an interdisciplinary team in a fast-paced environment, while making necessary connections
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
population and comparative genomics to examine genetic diversity, selection, pangenome relationships, and functional conservation. You will also develop reproducible GPU- and CPU-based high-performance
-
programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image
-
. Demonstrated experience with training and calibrating other large-scale complex models. Demonstrated ability to pretrain large-scale models from scratch, including distributed multi-GPU training. Demonstrated