Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Princeton University
- ;
- Ecole Centrale de Nantes
- Harvard University
- Lawrence Berkeley National Laboratory
- Nanyang Technological University
- National University of Singapore
- Singapore University of Technology & Design
- UNIVERSITY OF SOUTHAMPTON
- UNIVERSITY OF SURREY
- University of Maryland, Baltimore
- University of Massachusetts
- University of Surrey
- University of Texas at Austin
- 4 more »
- « less
-
Field
-
will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently received or be about to receive a Ph.D. or doctorate degree in cognitive science, psychology, computer science
-
structure, novel electronics and bioengineering applications. AI Postdoctoral Research Fellows will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently received or are about to
-
has also been developing physics-based machine learning algorithms for three dimensional seismic modeling, imaging and inversion using high performance computation including parallelization on GPUs
-
Research Fellows will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently received or be about to receive a Ph.D. or doctorate degree in cognitive science, psychology
-
to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering large AI models or their applications. Current PLI
-
material design and structure, novel electronics and bioengineering applications. AI Postdoctoral Research Fellows will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently
-
require the use of high-performance computing and GPU acceleration. The successful applicant will have access to unexplored and one-of-a-kind datasets of DAS in urban settings to explore their soundscapes
-
leverage massive investments and developments from the commercial technology sector, creating unprecedented opportunities for advancement. The development of specialized hardware for machine learning (GPUs
-
. Expertise in a GPU parallelization method (e.g., CUDA or ROCm). Experience in distributed computing for AI/ML workflows. Experience with literate computing tools, such as Jupyter Notebooks or RStudio Informal
-
part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300