Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Harvard University
- National University of Singapore
- Nanyang Technological University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Singapore University of Technology & Design
- UNIVERSITY OF SURREY
- University of Oslo
- Aarhus University
- CRANFIELD UNIVERSITY
- Center for Devices and Radiological Health (CDRH)
- Dana-Farber Cancer Institute (DFCI)
- Hong Kong Polytechnic University
- INESC TEC
- Imperial College London
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Oden Institute for Computational Engineering and Sciences
- SUNY University at Buffalo
- UCL;
- University of California
- University of Idaho
- University of Maryland, Baltimore
- University of Texas Rio Grande Valley
- University of Waterloo
- Zintellect
- 15 more »
- « less
-
Field
-
. 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
-
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
-
streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use
-
research. BOLD also has access to national compute resources (3M GPU hours on Isambard for the first 1.5 years) and we are working hard to get to 5000 H100 equivalent compute capacity in total across
-
for recruitment positions and for general criteria for the position. Preferred selection criteria Knowledge of in Norwegian/Scandinavian language Experience with GPU based systems Experience with HPC based
-
for recruitment positions and for general criteria for the position. Preferred selection criteria Knowledge of in Norwegian/Scandinavian language Experience with GPU based systems Experience with HPC based
-
, 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
-
-performance computing (HPC) clusters or cloud GPU instances. Analytical Rigor: Ability to design ablation studies to isolate the impact of individual loss components. Communication: Ability to clearly
-
systems, FPGA/RTL, and RF technologies, and CUDA coding for GPU acceleration is a plus. Strong leadership, communication, and documentation skills.