Sort by
Refine Your Search
-
Country
-
Employer
- Harvard University
- University of Oslo
- Aarhus University
- Dana-Farber Cancer Institute (DFCI)
- Florida Atlantic University
- Georgia Southern University
- Hong Kong Polytechnic University
- NTNU Norwegian University of Science and Technology
- Oden Institute for Computational Engineering and Sciences
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- SUNY University at Buffalo
- University of California
- University of Idaho
- University of Maryland, Baltimore
- University of Texas Rio Grande Valley
- University of Waterloo
- Zintellect
- 7 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
-
· 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
-
, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI Fellowship
-
Processing Unit (GPU) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed